Overview

Dataset statistics

Number of variables367
Number of observations42962
Missing cells2217179
Missing cells (%)14.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory120.3 MiB
Average record size in memory2.9 KiB

Variable types

Numeric204
Categorical163

Alerts

EINGEFUEGT_AM has a high cardinality: 1599 distinct values High cardinality
AKT_DAT_KL has 6969 (16.2%) missing values Missing
ALTER_HH has 6969 (16.2%) missing values Missing
ALTER_KIND1 has 40974 (95.4%) missing values Missing
ALTER_KIND2 has 42206 (98.2%) missing values Missing
ALTER_KIND3 has 42788 (99.6%) missing values Missing
ALTER_KIND4 has 42921 (99.9%) missing values Missing
ALTERSKATEGORIE_FEIN has 8155 (19.0%) missing values Missing
ANZ_HAUSHALTE_AKTIV has 7777 (18.1%) missing values Missing
ANZ_HH_TITEL has 8246 (19.2%) missing values Missing
ANZ_KINDER has 6969 (16.2%) missing values Missing
ANZ_PERSONEN has 6969 (16.2%) missing values Missing
ANZ_STATISTISCHE_HAUSHALTE has 7777 (18.1%) missing values Missing
ANZ_TITEL has 6969 (16.2%) missing values Missing
ARBEIT has 7951 (18.5%) missing values Missing
BALLRAUM has 7799 (18.2%) missing values Missing
CAMEO_DEU_2015 has 7888 (18.4%) missing values Missing
CAMEO_DEUG_2015 has 7888 (18.4%) missing values Missing
CAMEO_INTL_2015 has 7888 (18.4%) missing values Missing
CJT_GESAMTTYP has 605 (1.4%) missing values Missing
CJT_KATALOGNUTZER has 605 (1.4%) missing values Missing
CJT_TYP_1 has 605 (1.4%) missing values Missing
CJT_TYP_2 has 605 (1.4%) missing values Missing
CJT_TYP_3 has 605 (1.4%) missing values Missing
CJT_TYP_4 has 605 (1.4%) missing values Missing
CJT_TYP_5 has 605 (1.4%) missing values Missing
CJT_TYP_6 has 605 (1.4%) missing values Missing
D19_BANKEN_ONLINE_QUOTE_12 has 7584 (17.7%) missing values Missing
D19_GESAMT_ONLINE_QUOTE_12 has 7584 (17.7%) missing values Missing
D19_KONSUMTYP has 7584 (17.7%) missing values Missing
D19_LETZTER_KAUF_BRANCHE has 7584 (17.7%) missing values Missing
D19_LOTTO has 7584 (17.7%) missing values Missing
D19_SOZIALES has 7584 (17.7%) missing values Missing
D19_TELKO_ONLINE_QUOTE_12 has 7584 (17.7%) missing values Missing
D19_VERSAND_ONLINE_QUOTE_12 has 7584 (17.7%) missing values Missing
D19_VERSI_ONLINE_QUOTE_12 has 7584 (17.7%) missing values Missing
DSL_FLAG has 7777 (18.1%) missing values Missing
EINGEFUEGT_AM has 7777 (18.1%) missing values Missing
EINGEZOGENAM_HH_JAHR has 6969 (16.2%) missing values Missing
EWDICHTE has 7799 (18.2%) missing values Missing
EXTSEL992 has 15948 (37.1%) missing values Missing
FIRMENDICHTE has 7777 (18.1%) missing values Missing
GEBAEUDETYP has 7777 (18.1%) missing values Missing
GEBAEUDETYP_RASTER has 7777 (18.1%) missing values Missing
GEMEINDETYP has 7955 (18.5%) missing values Missing
GFK_URLAUBERTYP has 605 (1.4%) missing values Missing
HH_DELTA_FLAG has 9678 (22.5%) missing values Missing
HH_EINKOMMEN_SCORE has 704 (1.6%) missing values Missing
INNENSTADT has 7799 (18.2%) missing values Missing
KBA05_ALTER1 has 8648 (20.1%) missing values Missing
KBA05_ALTER2 has 8648 (20.1%) missing values Missing
KBA05_ALTER3 has 8648 (20.1%) missing values Missing
KBA05_ALTER4 has 8648 (20.1%) missing values Missing
KBA05_ANHANG has 8648 (20.1%) missing values Missing
KBA05_ANTG1 has 8648 (20.1%) missing values Missing
KBA05_ANTG2 has 8648 (20.1%) missing values Missing
KBA05_ANTG3 has 8648 (20.1%) missing values Missing
KBA05_ANTG4 has 8648 (20.1%) missing values Missing
KBA05_AUTOQUOT has 8648 (20.1%) missing values Missing
KBA05_BAUMAX has 8648 (20.1%) missing values Missing
KBA05_CCM1 has 8648 (20.1%) missing values Missing
KBA05_CCM2 has 8648 (20.1%) missing values Missing
KBA05_CCM3 has 8648 (20.1%) missing values Missing
KBA05_CCM4 has 8648 (20.1%) missing values Missing
KBA05_DIESEL has 8648 (20.1%) missing values Missing
KBA05_FRAU has 8648 (20.1%) missing values Missing
KBA05_GBZ has 8648 (20.1%) missing values Missing
KBA05_HERST1 has 8648 (20.1%) missing values Missing
KBA05_HERST2 has 8648 (20.1%) missing values Missing
KBA05_HERST3 has 8648 (20.1%) missing values Missing
KBA05_HERST4 has 8648 (20.1%) missing values Missing
KBA05_HERST5 has 8648 (20.1%) missing values Missing
KBA05_HERSTTEMP has 7777 (18.1%) missing values Missing
KBA05_KRSAQUOT has 8648 (20.1%) missing values Missing
KBA05_KRSHERST1 has 8648 (20.1%) missing values Missing
KBA05_KRSHERST2 has 8648 (20.1%) missing values Missing
KBA05_KRSHERST3 has 8648 (20.1%) missing values Missing
KBA05_KRSKLEIN has 8648 (20.1%) missing values Missing
KBA05_KRSOBER has 8648 (20.1%) missing values Missing
KBA05_KRSVAN has 8648 (20.1%) missing values Missing
KBA05_KRSZUL has 8648 (20.1%) missing values Missing
KBA05_KW1 has 8648 (20.1%) missing values Missing
KBA05_KW2 has 8648 (20.1%) missing values Missing
KBA05_KW3 has 8648 (20.1%) missing values Missing
KBA05_MAXAH has 8648 (20.1%) missing values Missing
KBA05_MAXBJ has 8648 (20.1%) missing values Missing
KBA05_MAXHERST has 8648 (20.1%) missing values Missing
KBA05_MAXSEG has 8648 (20.1%) missing values Missing
KBA05_MAXVORB has 8648 (20.1%) missing values Missing
KBA05_MOD1 has 8648 (20.1%) missing values Missing
KBA05_MOD2 has 8648 (20.1%) missing values Missing
KBA05_MOD3 has 8648 (20.1%) missing values Missing
KBA05_MOD4 has 8648 (20.1%) missing values Missing
KBA05_MOD8 has 8648 (20.1%) missing values Missing
KBA05_MODTEMP has 7777 (18.1%) missing values Missing
KBA05_MOTOR has 8648 (20.1%) missing values Missing
KBA05_MOTRAD has 8648 (20.1%) missing values Missing
KBA05_SEG1 has 8648 (20.1%) missing values Missing
KBA05_SEG10 has 8648 (20.1%) missing values Missing
KBA05_SEG2 has 8648 (20.1%) missing values Missing
KBA05_SEG3 has 8648 (20.1%) missing values Missing
KBA05_SEG4 has 8648 (20.1%) missing values Missing
KBA05_SEG5 has 8648 (20.1%) missing values Missing
KBA05_SEG6 has 8648 (20.1%) missing values Missing
KBA05_SEG7 has 8648 (20.1%) missing values Missing
KBA05_SEG8 has 8648 (20.1%) missing values Missing
KBA05_SEG9 has 8648 (20.1%) missing values Missing
KBA05_VORB0 has 8648 (20.1%) missing values Missing
KBA05_VORB1 has 8648 (20.1%) missing values Missing
KBA05_VORB2 has 8648 (20.1%) missing values Missing
KBA05_ZUL1 has 8648 (20.1%) missing values Missing
KBA05_ZUL2 has 8648 (20.1%) missing values Missing
KBA05_ZUL3 has 8648 (20.1%) missing values Missing
KBA05_ZUL4 has 8648 (20.1%) missing values Missing
KBA13_ALTERHALTER_30 has 7962 (18.5%) missing values Missing
KBA13_ALTERHALTER_45 has 7962 (18.5%) missing values Missing
KBA13_ALTERHALTER_60 has 7962 (18.5%) missing values Missing
KBA13_ALTERHALTER_61 has 7962 (18.5%) missing values Missing
KBA13_ANTG1 has 7962 (18.5%) missing values Missing
KBA13_ANTG2 has 7962 (18.5%) missing values Missing
KBA13_ANTG3 has 7962 (18.5%) missing values Missing
KBA13_ANTG4 has 7962 (18.5%) missing values Missing
KBA13_ANZAHL_PKW has 7962 (18.5%) missing values Missing
KBA13_AUDI has 7962 (18.5%) missing values Missing
KBA13_AUTOQUOTE has 7962 (18.5%) missing values Missing
KBA13_BAUMAX has 7962 (18.5%) missing values Missing
KBA13_BJ_1999 has 7962 (18.5%) missing values Missing
KBA13_BJ_2000 has 7962 (18.5%) missing values Missing
KBA13_BJ_2004 has 7962 (18.5%) missing values Missing
KBA13_BJ_2006 has 7962 (18.5%) missing values Missing
KBA13_BJ_2008 has 7962 (18.5%) missing values Missing
KBA13_BJ_2009 has 7962 (18.5%) missing values Missing
KBA13_BMW has 7962 (18.5%) missing values Missing
KBA13_CCM_0_1400 has 7962 (18.5%) missing values Missing
KBA13_CCM_1000 has 7962 (18.5%) missing values Missing
KBA13_CCM_1200 has 7962 (18.5%) missing values Missing
KBA13_CCM_1400 has 7962 (18.5%) missing values Missing
KBA13_CCM_1401_2500 has 7962 (18.5%) missing values Missing
KBA13_CCM_1500 has 7962 (18.5%) missing values Missing
KBA13_CCM_1600 has 7962 (18.5%) missing values Missing
KBA13_CCM_1800 has 7962 (18.5%) missing values Missing
KBA13_CCM_2000 has 7962 (18.5%) missing values Missing
KBA13_CCM_2500 has 7962 (18.5%) missing values Missing
KBA13_CCM_2501 has 7962 (18.5%) missing values Missing
KBA13_CCM_3000 has 7962 (18.5%) missing values Missing
KBA13_CCM_3001 has 7962 (18.5%) missing values Missing
KBA13_FAB_ASIEN has 7962 (18.5%) missing values Missing
KBA13_FAB_SONSTIGE has 7962 (18.5%) missing values Missing
KBA13_FIAT has 7962 (18.5%) missing values Missing
KBA13_FORD has 7962 (18.5%) missing values Missing
KBA13_GBZ has 7962 (18.5%) missing values Missing
KBA13_HALTER_20 has 7962 (18.5%) missing values Missing
KBA13_HALTER_25 has 7962 (18.5%) missing values Missing
KBA13_HALTER_30 has 7962 (18.5%) missing values Missing
KBA13_HALTER_35 has 7962 (18.5%) missing values Missing
KBA13_HALTER_40 has 7962 (18.5%) missing values Missing
KBA13_HALTER_45 has 7962 (18.5%) missing values Missing
KBA13_HALTER_50 has 7962 (18.5%) missing values Missing
KBA13_HALTER_55 has 7962 (18.5%) missing values Missing
KBA13_HALTER_60 has 7962 (18.5%) missing values Missing
KBA13_HALTER_65 has 7962 (18.5%) missing values Missing
KBA13_HALTER_66 has 7962 (18.5%) missing values Missing
KBA13_HERST_ASIEN has 7962 (18.5%) missing values Missing
KBA13_HERST_AUDI_VW has 7962 (18.5%) missing values Missing
KBA13_HERST_BMW_BENZ has 7962 (18.5%) missing values Missing
KBA13_HERST_EUROPA has 7962 (18.5%) missing values Missing
KBA13_HERST_FORD_OPEL has 7962 (18.5%) missing values Missing
KBA13_HERST_SONST has 7962 (18.5%) missing values Missing
KBA13_HHZ has 7962 (18.5%) missing values Missing
KBA13_KMH_0_140 has 7962 (18.5%) missing values Missing
KBA13_KMH_110 has 7962 (18.5%) missing values Missing
KBA13_KMH_140 has 7962 (18.5%) missing values Missing
KBA13_KMH_140_210 has 7962 (18.5%) missing values Missing
KBA13_KMH_180 has 7962 (18.5%) missing values Missing
KBA13_KMH_210 has 7962 (18.5%) missing values Missing
KBA13_KMH_211 has 7962 (18.5%) missing values Missing
KBA13_KMH_250 has 7962 (18.5%) missing values Missing
KBA13_KMH_251 has 7962 (18.5%) missing values Missing
KBA13_KRSAQUOT has 7962 (18.5%) missing values Missing
KBA13_KRSHERST_AUDI_VW has 7962 (18.5%) missing values Missing
KBA13_KRSHERST_BMW_BENZ has 7962 (18.5%) missing values Missing
KBA13_KRSHERST_FORD_OPEL has 7962 (18.5%) missing values Missing
KBA13_KRSSEG_KLEIN has 7962 (18.5%) missing values Missing
KBA13_KRSSEG_OBER has 7962 (18.5%) missing values Missing
KBA13_KRSSEG_VAN has 7962 (18.5%) missing values Missing
KBA13_KRSZUL_NEU has 7962 (18.5%) missing values Missing
KBA13_KW_0_60 has 7962 (18.5%) missing values Missing
KBA13_KW_110 has 7962 (18.5%) missing values Missing
KBA13_KW_120 has 7962 (18.5%) missing values Missing
KBA13_KW_121 has 7962 (18.5%) missing values Missing
KBA13_KW_30 has 7962 (18.5%) missing values Missing
KBA13_KW_40 has 7962 (18.5%) missing values Missing
KBA13_KW_50 has 7962 (18.5%) missing values Missing
KBA13_KW_60 has 7962 (18.5%) missing values Missing
KBA13_KW_61_120 has 7962 (18.5%) missing values Missing
KBA13_KW_70 has 7962 (18.5%) missing values Missing
KBA13_KW_80 has 7962 (18.5%) missing values Missing
KBA13_KW_90 has 7962 (18.5%) missing values Missing
KBA13_MAZDA has 7962 (18.5%) missing values Missing
KBA13_MERCEDES has 7962 (18.5%) missing values Missing
KBA13_MOTOR has 7962 (18.5%) missing values Missing
KBA13_NISSAN has 7962 (18.5%) missing values Missing
KBA13_OPEL has 7962 (18.5%) missing values Missing
KBA13_PEUGEOT has 7962 (18.5%) missing values Missing
KBA13_RENAULT has 7962 (18.5%) missing values Missing
KBA13_SEG_GELAENDEWAGEN has 7962 (18.5%) missing values Missing
KBA13_SEG_GROSSRAUMVANS has 7962 (18.5%) missing values Missing
KBA13_SEG_KLEINST has 7962 (18.5%) missing values Missing
KBA13_SEG_KLEINWAGEN has 7962 (18.5%) missing values Missing
KBA13_SEG_KOMPAKTKLASSE has 7962 (18.5%) missing values Missing
KBA13_SEG_MINIVANS has 7962 (18.5%) missing values Missing
KBA13_SEG_MINIWAGEN has 7962 (18.5%) missing values Missing
KBA13_SEG_MITTELKLASSE has 7962 (18.5%) missing values Missing
KBA13_SEG_OBEREMITTELKLASSE has 7962 (18.5%) missing values Missing
KBA13_SEG_OBERKLASSE has 7962 (18.5%) missing values Missing
KBA13_SEG_SONSTIGE has 7962 (18.5%) missing values Missing
KBA13_SEG_SPORTWAGEN has 7962 (18.5%) missing values Missing
KBA13_SEG_UTILITIES has 7962 (18.5%) missing values Missing
KBA13_SEG_VAN has 7962 (18.5%) missing values Missing
KBA13_SEG_WOHNMOBILE has 7962 (18.5%) missing values Missing
KBA13_SITZE_4 has 7962 (18.5%) missing values Missing
KBA13_SITZE_5 has 7962 (18.5%) missing values Missing
KBA13_SITZE_6 has 7962 (18.5%) missing values Missing
KBA13_TOYOTA has 7962 (18.5%) missing values Missing
KBA13_VORB_0 has 7962 (18.5%) missing values Missing
KBA13_VORB_1 has 7962 (18.5%) missing values Missing
KBA13_VORB_1_2 has 7962 (18.5%) missing values Missing
KBA13_VORB_2 has 7962 (18.5%) missing values Missing
KBA13_VORB_3 has 7962 (18.5%) missing values Missing
KBA13_VW has 7962 (18.5%) missing values Missing
KK_KUNDENTYP has 25316 (58.9%) missing values Missing
KKK has 8445 (19.7%) missing values Missing
KONSUMNAEHE has 6997 (16.3%) missing values Missing
KONSUMZELLE has 7777 (18.1%) missing values Missing
LP_FAMILIE_FEIN has 605 (1.4%) missing values Missing
LP_FAMILIE_GROB has 605 (1.4%) missing values Missing
LP_LEBENSPHASE_FEIN has 605 (1.4%) missing values Missing
LP_LEBENSPHASE_GROB has 605 (1.4%) missing values Missing
LP_STATUS_FEIN has 605 (1.4%) missing values Missing
LP_STATUS_GROB has 605 (1.4%) missing values Missing
MIN_GEBAEUDEJAHR has 7777 (18.1%) missing values Missing
MOBI_RASTER has 7777 (18.1%) missing values Missing
MOBI_REGIO has 8648 (20.1%) missing values Missing
ONLINE_AFFINITAET has 605 (1.4%) missing values Missing
ORTSGR_KLS9 has 7951 (18.5%) missing values Missing
OST_WEST_KZ has 7777 (18.1%) missing values Missing
PLZ8_ANTG1 has 8153 (19.0%) missing values Missing
PLZ8_ANTG2 has 8153 (19.0%) missing values Missing
PLZ8_ANTG3 has 8153 (19.0%) missing values Missing
PLZ8_ANTG4 has 8153 (19.0%) missing values Missing
PLZ8_BAUMAX has 8153 (19.0%) missing values Missing
PLZ8_GBZ has 8153 (19.0%) missing values Missing
PLZ8_HHZ has 8153 (19.0%) missing values Missing
REGIOTYP has 8445 (19.7%) missing values Missing
RELAT_AB has 7951 (18.5%) missing values Missing
RETOURTYP_BK_S has 605 (1.4%) missing values Missing
RT_KEIN_ANREIZ has 605 (1.4%) missing values Missing
RT_SCHNAEPPCHEN has 605 (1.4%) missing values Missing
RT_UEBERGROESSE has 6380 (14.9%) missing values Missing
SOHO_KZ has 6969 (16.2%) missing values Missing
STRUKTURTYP has 7955 (18.5%) missing values Missing
TITEL_KZ has 6969 (16.2%) missing values Missing
UMFELD_ALT has 7925 (18.4%) missing values Missing
UMFELD_JUNG has 7925 (18.4%) missing values Missing
UNGLEICHENN_FLAG has 6969 (16.2%) missing values Missing
VERDICHTUNGSRAUM has 7955 (18.5%) missing values Missing
VHA has 6969 (16.2%) missing values Missing
VHN has 8445 (19.7%) missing values Missing
VK_DHT4A has 7267 (16.9%) missing values Missing
VK_DISTANZ has 7267 (16.9%) missing values Missing
VK_ZG11 has 7267 (16.9%) missing values Missing
W_KEIT_KIND_HH has 9678 (22.5%) missing values Missing
WOHNDAUER_2008 has 6969 (16.2%) missing values Missing
WOHNLAGE has 7777 (18.1%) missing values Missing
ANZ_HH_TITEL is highly skewed (γ1 = 21.40627383) Skewed
D19_VERSI_ONLINE_DATUM is highly skewed (γ1 = -20.21632487) Skewed
LNR has unique values Unique
ALTER_HH has 6208 (14.4%) zeros Zeros
ALTERSKATEGORIE_FEIN has 3536 (8.2%) zeros Zeros
ANZ_HAUSHALTE_AKTIV has 530 (1.2%) zeros Zeros
ANZ_HH_TITEL has 33486 (77.9%) zeros Zeros
ANZ_KINDER has 33821 (78.7%) zeros Zeros
ANZ_PERSONEN has 2709 (6.3%) zeros Zeros
D19_BANKEN_ANZ_12 has 40198 (93.6%) zeros Zeros
D19_BANKEN_ANZ_24 has 38714 (90.1%) zeros Zeros
D19_BANKEN_DIREKT has 36805 (85.7%) zeros Zeros
D19_BANKEN_GROSS has 38760 (90.2%) zeros Zeros
D19_BANKEN_LOKAL has 42170 (98.2%) zeros Zeros
D19_BANKEN_ONLINE_QUOTE_12 has 33787 (78.6%) zeros Zeros
D19_BANKEN_REST has 39983 (93.1%) zeros Zeros
D19_BEKLEIDUNG_GEH has 35949 (83.7%) zeros Zeros
D19_BEKLEIDUNG_REST has 29981 (69.8%) zeros Zeros
D19_BILDUNG has 34895 (81.2%) zeros Zeros
D19_BIO_OEKO has 38422 (89.4%) zeros Zeros
D19_BUCH_CD has 22410 (52.2%) zeros Zeros
D19_DIGIT_SERV has 41226 (96.0%) zeros Zeros
D19_DROGERIEARTIKEL has 36725 (85.5%) zeros Zeros
D19_ENERGIE has 39064 (90.9%) zeros Zeros
D19_FREIZEIT has 37678 (87.7%) zeros Zeros
D19_GARTEN has 39737 (92.5%) zeros Zeros
D19_GESAMT_ANZ_12 has 25341 (59.0%) zeros Zeros
D19_GESAMT_ANZ_24 has 20736 (48.3%) zeros Zeros
D19_GESAMT_ONLINE_QUOTE_12 has 23291 (54.2%) zeros Zeros
D19_HANDWERK has 31086 (72.4%) zeros Zeros
D19_HAUS_DEKO has 28556 (66.5%) zeros Zeros
D19_KINDERARTIKEL has 33786 (78.6%) zeros Zeros
D19_KOSMETIK has 28919 (67.3%) zeros Zeros
D19_LEBENSMITTEL has 37467 (87.2%) zeros Zeros
D19_LOTTO has 20293 (47.2%) zeros Zeros
D19_NAHRUNGSERGAENZUNG has 38508 (89.6%) zeros Zeros
D19_RATGEBER has 35306 (82.2%) zeros Zeros
D19_REISEN has 28298 (65.9%) zeros Zeros
D19_SAMMELARTIKEL has 31986 (74.5%) zeros Zeros
D19_SCHUHE has 37508 (87.3%) zeros Zeros
D19_SONSTIGE has 15258 (35.5%) zeros Zeros
D19_SOZIALES has 9615 (22.4%) zeros Zeros
D19_TECHNIK has 25369 (59.0%) zeros Zeros
D19_TELKO_ANZ_12 has 41099 (95.7%) zeros Zeros
D19_TELKO_ANZ_24 has 39720 (92.5%) zeros Zeros
D19_TELKO_MOBILE has 36119 (84.1%) zeros Zeros
D19_TELKO_REST has 37986 (88.4%) zeros Zeros
D19_TIERARTIKEL has 40917 (95.2%) zeros Zeros
D19_VERSAND_ANZ_12 has 27763 (64.6%) zeros Zeros
D19_VERSAND_ANZ_24 has 23413 (54.5%) zeros Zeros
D19_VERSAND_ONLINE_QUOTE_12 has 24597 (57.3%) zeros Zeros
D19_VERSAND_REST has 36458 (84.9%) zeros Zeros
D19_VERSI_ANZ_12 has 39485 (91.9%) zeros Zeros
D19_VERSI_ANZ_24 has 37473 (87.2%) zeros Zeros
D19_VERSICHERUNGEN has 32015 (74.5%) zeros Zeros
D19_VOLLSORTIMENT has 20628 (48.0%) zeros Zeros
D19_WEIN_FEINKOST has 36950 (86.0%) zeros Zeros
GEBURTSJAHR has 17475 (40.7%) zeros Zeros
KBA05_ALTER1 has 5246 (12.2%) zeros Zeros
KBA05_ALTER4 has 830 (1.9%) zeros Zeros
KBA05_BAUMAX has 14332 (33.4%) zeros Zeros
KBA05_CCM4 has 10887 (25.3%) zeros Zeros
KBA05_DIESEL has 2354 (5.5%) zeros Zeros
KBA05_HERST1 has 2645 (6.2%) zeros Zeros
KBA05_HERST3 has 600 (1.4%) zeros Zeros
KBA05_HERST4 has 1037 (2.4%) zeros Zeros
KBA05_HERST5 has 1641 (3.8%) zeros Zeros
KBA05_KW3 has 7676 (17.9%) zeros Zeros
KBA05_MOD1 has 11398 (26.5%) zeros Zeros
KBA05_MOD4 has 1672 (3.9%) zeros Zeros
KBA05_SEG10 has 3972 (9.2%) zeros Zeros
KBA05_SEG5 has 7092 (16.5%) zeros Zeros
KBA05_VORB2 has 2781 (6.5%) zeros Zeros
KBA05_ZUL3 has 2023 (4.7%) zeros Zeros
KBA05_ZUL4 has 3181 (7.4%) zeros Zeros
KBA13_BJ_2008 has 5588 (13.0%) zeros Zeros
KBA13_BJ_2009 has 4183 (9.7%) zeros Zeros
KBA13_CCM_0_1400 has 6654 (15.5%) zeros Zeros
KBA13_CCM_1000 has 4518 (10.5%) zeros Zeros
KBA13_CCM_1200 has 6800 (15.8%) zeros Zeros
KBA13_CCM_1800 has 5981 (13.9%) zeros Zeros
KBA13_CCM_2500 has 4153 (9.7%) zeros Zeros
KBA13_CCM_2501 has 3906 (9.1%) zeros Zeros
KBA13_CCM_3000 has 2500 (5.8%) zeros Zeros
KBA13_KMH_0_140 has 4632 (10.8%) zeros Zeros
KBA13_KMH_211 has 5978 (13.9%) zeros Zeros
KBA13_KMH_250 has 5985 (13.9%) zeros Zeros
KBA13_KW_110 has 5425 (12.6%) zeros Zeros
KBA13_KW_120 has 3940 (9.2%) zeros Zeros
KBA13_KW_121 has 4018 (9.4%) zeros Zeros
KBA13_KW_40 has 4534 (10.6%) zeros Zeros
KBA13_KW_50 has 6649 (15.5%) zeros Zeros
KBA13_KW_60 has 6069 (14.1%) zeros Zeros
KBA13_KW_70 has 6422 (14.9%) zeros Zeros
KBA13_KW_80 has 5746 (13.4%) zeros Zeros
KBA13_KW_90 has 5936 (13.8%) zeros Zeros
KBA13_SEG_OBERKLASSE has 3823 (8.9%) zeros Zeros
KBA13_SEG_SPORTWAGEN has 3526 (8.2%) zeros Zeros
KBA13_SEG_WOHNMOBILE has 3870 (9.0%) zeros Zeros
KBA13_VORB_3 has 7228 (16.8%) zeros Zeros
LP_FAMILIE_FEIN has 8208 (19.1%) zeros Zeros
LP_FAMILIE_GROB has 8208 (19.1%) zeros Zeros
LP_LEBENSPHASE_FEIN has 8298 (19.3%) zeros Zeros
LP_LEBENSPHASE_GROB has 8273 (19.3%) zeros Zeros
ONLINE_AFFINITAET has 2156 (5.0%) zeros Zeros
PRAEGENDE_JUGENDJAHRE has 7454 (17.4%) zeros Zeros
REGIOTYP has 1485 (3.5%) zeros Zeros
VERDICHTUNGSRAUM has 17087 (39.8%) zeros Zeros
VHA has 16176 (37.7%) zeros Zeros
W_KEIT_KIND_HH has 739 (1.7%) zeros Zeros

Reproduction

Analysis started2021-12-29 12:58:56.338773
Analysis finished2021-12-29 12:59:22.106393
Duration25.77 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

LNR
Real number (ℝ≥0)

UNIQUE

Distinct42962
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42803.12013
Minimum1
Maximum85795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:22.350191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4203.05
Q121284.25
median42710
Q364340.5
95-th percentile81345.9
Maximum85795
Range85794
Interquartile range (IQR)43056.25

Descriptive statistics

Standard deviation24778.33998
Coefficient of variation (CV)0.5788909759
Kurtosis-1.203373177
Mean42803.12013
Median Absolute Deviation (MAD)21506
Skewness0.002154314123
Sum1838907647
Variance613966132.4
MonotonicityNot monotonic
2021-12-29T15:59:22.483133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
811971
 
< 0.1%
689071
 
< 0.1%
730011
 
< 0.1%
709521
 
< 0.1%
279431
 
< 0.1%
320371
 
< 0.1%
217921
 
< 0.1%
443191
 
< 0.1%
422701
 
< 0.1%
Other values (42952)42952
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
51
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
131
< 0.1%
141
< 0.1%
191
< 0.1%
211
< 0.1%
ValueCountFrequency (%)
857951
< 0.1%
857921
< 0.1%
857881
< 0.1%
857861
< 0.1%
857821
< 0.1%
857761
< 0.1%
857751
< 0.1%
857741
< 0.1%
857731
< 0.1%
857711
< 0.1%

AGER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
-1
17963 
2
12470 
1
9229 
3
2373 
0
 
927

Length

Max length2
Median length1
Mean length1.418113682
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
-117963
41.8%
212470
29.0%
19229
21.5%
32373
 
5.5%
0927
 
2.2%

Length

2021-12-29T15:59:22.585874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:22.657315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
127192
63.3%
212470
29.0%
32373
 
5.5%
0927
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AKT_DAT_KL
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean1.525241019
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:22.728981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.741499841
Coefficient of variation (CV)1.141786655
Kurtosis10.97107406
Mean1.525241019
Median Absolute Deviation (MAD)0
Skewness3.476977401
Sum54898
Variance3.032821697
MonotonicityNot monotonic
2021-12-29T15:59:22.818645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
131999
74.5%
91195
 
2.8%
2788
 
1.8%
5502
 
1.2%
3403
 
0.9%
6298
 
0.7%
4281
 
0.7%
7279
 
0.6%
8248
 
0.6%
(Missing)6969
 
16.2%
ValueCountFrequency (%)
131999
74.5%
2788
 
1.8%
3403
 
0.9%
4281
 
0.7%
5502
 
1.2%
6298
 
0.7%
7279
 
0.6%
8248
 
0.6%
91195
 
2.8%
ValueCountFrequency (%)
91195
 
2.8%
8248
 
0.6%
7279
 
0.6%
6298
 
0.7%
5502
 
1.2%
4281
 
0.7%
3403
 
0.9%
2788
 
1.8%
131999
74.5%

ALTER_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct20
Distinct (%)0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean10.28555552
Minimum0
Maximum21
Zeros6208
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:22.922260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median10
Q315
95-th percentile20
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.08260965
Coefficient of variation (CV)0.591373955
Kurtosis-0.6426953343
Mean10.28555552
Median Absolute Deviation (MAD)3
Skewness-0.2225836314
Sum370208
Variance36.99814015
MonotonicityNot monotonic
2021-12-29T15:59:23.013372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
06208
14.4%
93922
9.1%
103775
8.8%
82701
 
6.3%
112276
 
5.3%
122135
 
5.0%
71749
 
4.1%
131644
 
3.8%
151559
 
3.6%
141508
 
3.5%
Other values (10)8516
19.8%
(Missing)6969
16.2%
ValueCountFrequency (%)
06208
14.4%
34
 
< 0.1%
426
 
0.1%
5122
 
0.3%
6705
 
1.6%
71749
 
4.1%
82701
6.3%
93922
9.1%
103775
8.8%
112276
 
5.3%
ValueCountFrequency (%)
211412
3.3%
201216
2.8%
191255
2.9%
181175
2.7%
171245
2.9%
161356
3.2%
151559
3.6%
141508
3.5%
131644
3.8%
122135
5.0%

ALTER_KIND1
Real number (ℝ≥0)

MISSING

Distinct17
Distinct (%)0.9%
Missing40974
Missing (%)95.4%
Infinite0
Infinite (%)0.0%
Mean12.60613682
Minimum2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:23.104485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median13
Q316
95-th percentile18
Maximum18
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.924976167
Coefficient of variation (CV)0.3113544001
Kurtosis-0.9142784002
Mean12.60613682
Median Absolute Deviation (MAD)3
Skewness-0.3489802568
Sum25061
Variance15.40543791
MonotonicityNot monotonic
2021-12-29T15:59:23.195685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
18223
 
0.5%
17197
 
0.5%
15186
 
0.4%
14165
 
0.4%
16160
 
0.4%
13154
 
0.4%
10138
 
0.3%
12133
 
0.3%
11129
 
0.3%
9127
 
0.3%
Other values (7)376
 
0.9%
(Missing)40974
95.4%
ValueCountFrequency (%)
24
 
< 0.1%
312
 
< 0.1%
420
 
< 0.1%
524
 
0.1%
683
0.2%
7108
0.3%
8125
0.3%
9127
0.3%
10138
0.3%
11129
0.3%
ValueCountFrequency (%)
18223
0.5%
17197
0.5%
16160
0.4%
15186
0.4%
14165
0.4%
13154
0.4%
12133
0.3%
11129
0.3%
10138
0.3%
9127
0.3%

ALTER_KIND2
Real number (ℝ≥0)

MISSING

Distinct14
Distinct (%)1.9%
Missing42206
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean13.78306878
Minimum5
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:23.287363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q112
median14
Q316
95-th percentile18
Maximum18
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.065817368
Coefficient of variation (CV)0.2224335825
Kurtosis-0.5390706746
Mean13.78306878
Median Absolute Deviation (MAD)2
Skewness-0.461868109
Sum10420
Variance9.399236133
MonotonicityNot monotonic
2021-12-29T15:59:23.379471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1799
 
0.2%
1287
 
0.2%
1887
 
0.2%
1480
 
0.2%
1579
 
0.2%
1677
 
0.2%
1372
 
0.2%
1158
 
0.1%
1042
 
0.1%
931
 
0.1%
Other values (4)44
 
0.1%
(Missing)42206
98.2%
ValueCountFrequency (%)
54
 
< 0.1%
65
 
< 0.1%
713
 
< 0.1%
822
 
0.1%
931
 
0.1%
1042
0.1%
1158
0.1%
1287
0.2%
1372
0.2%
1480
0.2%
ValueCountFrequency (%)
1887
0.2%
1799
0.2%
1677
0.2%
1579
0.2%
1480
0.2%
1372
0.2%
1287
0.2%
1158
0.1%
1042
0.1%
931
 
0.1%

ALTER_KIND3
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)6.9%
Missing42788
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean14.65517241
Minimum6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:23.460799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10
Q113
median15
Q317
95-th percentile18
Maximum18
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.615328696
Coefficient of variation (CV)0.1784577228
Kurtosis0.5828992821
Mean14.65517241
Median Absolute Deviation (MAD)2
Skewness-0.7684269368
Sum2550
Variance6.83994419
MonotonicityNot monotonic
2021-12-29T15:59:23.562189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1828
 
0.1%
1427
 
0.1%
1724
 
0.1%
1323
 
0.1%
1522
 
0.1%
1619
 
< 0.1%
1214
 
< 0.1%
116
 
< 0.1%
105
 
< 0.1%
73
 
< 0.1%
Other values (2)3
 
< 0.1%
(Missing)42788
99.6%
ValueCountFrequency (%)
61
 
< 0.1%
73
 
< 0.1%
82
 
< 0.1%
105
 
< 0.1%
116
 
< 0.1%
1214
< 0.1%
1323
0.1%
1427
0.1%
1522
0.1%
1619
< 0.1%
ValueCountFrequency (%)
1828
0.1%
1724
0.1%
1619
< 0.1%
1522
0.1%
1427
0.1%
1323
0.1%
1214
< 0.1%
116
 
< 0.1%
105
 
< 0.1%
82
 
< 0.1%

ALTER_KIND4
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)26.8%
Missing42921
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean14.19512195
Minimum6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:23.644232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q113
median15
Q317
95-th percentile18
Maximum18
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.034958914
Coefficient of variation (CV)0.2138029476
Kurtosis0.8474975887
Mean14.19512195
Median Absolute Deviation (MAD)2
Skewness-0.9796221834
Sum582
Variance9.21097561
MonotonicityNot monotonic
2021-12-29T15:59:23.735673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
156
 
< 0.1%
136
 
< 0.1%
176
 
< 0.1%
145
 
< 0.1%
185
 
< 0.1%
164
 
< 0.1%
123
 
< 0.1%
72
 
< 0.1%
112
 
< 0.1%
61
 
< 0.1%
(Missing)42921
99.9%
ValueCountFrequency (%)
61
 
< 0.1%
72
 
< 0.1%
101
 
< 0.1%
112
 
< 0.1%
123
< 0.1%
136
< 0.1%
145
< 0.1%
156
< 0.1%
164
< 0.1%
176
< 0.1%
ValueCountFrequency (%)
185
< 0.1%
176
< 0.1%
164
< 0.1%
156
< 0.1%
145
< 0.1%
136
< 0.1%
123
< 0.1%
112
 
< 0.1%
101
 
< 0.1%
72
 
< 0.1%

ALTERSKATEGORIE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct25
Distinct (%)0.1%
Missing8155
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean9.855057891
Minimum0
Maximum25
Zeros3536
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:23.837582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median10
Q313
95-th percentile16
Maximum25
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.373539165
Coefficient of variation (CV)0.443786248
Kurtosis0.705776336
Mean9.855057891
Median Absolute Deviation (MAD)2
Skewness-0.6138376707
Sum343025
Variance19.12784483
MonotonicityNot monotonic
2021-12-29T15:59:23.940042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
95045
11.7%
104681
10.9%
03536
8.2%
83332
7.8%
123188
 
7.4%
113049
 
7.1%
132653
 
6.2%
142231
 
5.2%
72066
 
4.8%
151605
 
3.7%
Other values (15)3421
8.0%
(Missing)8155
19.0%
ValueCountFrequency (%)
03536
8.2%
21
 
< 0.1%
39
 
< 0.1%
433
 
0.1%
5136
 
0.3%
6819
 
1.9%
72066
4.8%
83332
7.8%
95045
11.7%
104681
10.9%
ValueCountFrequency (%)
2518
 
< 0.1%
2421
 
< 0.1%
234
 
< 0.1%
223
 
< 0.1%
2161
 
0.1%
20175
 
0.4%
19276
 
0.6%
18405
0.9%
17557
1.3%
16903
2.1%

ANZ_HAUSHALTE_AKTIV
Real number (ℝ≥0)

MISSING
ZEROS

Distinct175
Distinct (%)0.5%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean6.706096348
Minimum0
Maximum438
Zeros530
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:24.060063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q37
95-th percentile26
Maximum438
Range438
Interquartile range (IQR)6

Descriptive statistics

Standard deviation15.15178973
Coefficient of variation (CV)2.259405315
Kurtosis137.5348484
Mean6.706096348
Median Absolute Deviation (MAD)1
Skewness9.140196724
Sum235954
Variance229.5767321
MonotonicityNot monotonic
2021-12-29T15:59:24.173993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113816
32.2%
25437
 
12.7%
32188
 
5.1%
41358
 
3.2%
61193
 
2.8%
51190
 
2.8%
71155
 
2.7%
81070
 
2.5%
9944
 
2.2%
10821
 
1.9%
Other values (165)6013
14.0%
(Missing)7777
18.1%
ValueCountFrequency (%)
0530
 
1.2%
113816
32.2%
25437
 
12.7%
32188
 
5.1%
41358
 
3.2%
51190
 
2.8%
61193
 
2.8%
71155
 
2.7%
81070
 
2.5%
9944
 
2.2%
ValueCountFrequency (%)
4381
 
< 0.1%
3531
 
< 0.1%
3472
< 0.1%
3441
 
< 0.1%
3331
 
< 0.1%
3211
 
< 0.1%
3111
 
< 0.1%
3052
< 0.1%
3041
 
< 0.1%
2903
< 0.1%

ANZ_HH_TITEL
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing8246
Missing (%)19.2%
Infinite0
Infinite (%)0.0%
Mean0.0495736836
Minimum0
Maximum20
Zeros33486
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:24.375395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3803348241
Coefficient of variation (CV)7.67211142
Kurtosis739.8139452
Mean0.0495736836
Median Absolute Deviation (MAD)0
Skewness21.40627383
Sum1721
Variance0.1446545784
MonotonicityNot monotonic
2021-12-29T15:59:24.459394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
033486
77.9%
11038
 
2.4%
2101
 
0.2%
339
 
0.1%
417
 
< 0.1%
59
 
< 0.1%
66
 
< 0.1%
74
 
< 0.1%
94
 
< 0.1%
84
 
< 0.1%
Other values (5)8
 
< 0.1%
(Missing)8246
 
19.2%
ValueCountFrequency (%)
033486
77.9%
11038
 
2.4%
2101
 
0.2%
339
 
0.1%
417
 
< 0.1%
59
 
< 0.1%
66
 
< 0.1%
74
 
< 0.1%
84
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
172
 
< 0.1%
141
 
< 0.1%
133
 
< 0.1%
121
 
< 0.1%
94
< 0.1%
84
< 0.1%
74
< 0.1%
66
< 0.1%
59
< 0.1%

ANZ_KINDER
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean0.08898952574
Minimum0
Maximum6
Zeros33821
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:24.570550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3959945651
Coefficient of variation (CV)4.449900838
Kurtosis38.07321442
Mean0.08898952574
Median Absolute Deviation (MAD)0
Skewness5.577484144
Sum3203
Variance0.1568116956
MonotonicityNot monotonic
2021-12-29T15:59:24.651701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
033821
78.7%
11391
 
3.2%
2593
 
1.4%
3144
 
0.3%
428
 
0.1%
514
 
< 0.1%
62
 
< 0.1%
(Missing)6969
 
16.2%
ValueCountFrequency (%)
033821
78.7%
11391
 
3.2%
2593
 
1.4%
3144
 
0.3%
428
 
0.1%
514
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
514
 
< 0.1%
428
 
0.1%
3144
 
0.3%
2593
 
1.4%
11391
 
3.2%
033821
78.7%

ANZ_PERSONEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean2.017086656
Minimum0
Maximum24
Zeros2709
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:24.741226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.358095553
Coefficient of variation (CV)0.6732955914
Kurtosis3.649381657
Mean2.017086656
Median Absolute Deviation (MAD)1
Skewness1.198737521
Sum72601
Variance1.84442353
MonotonicityNot monotonic
2021-12-29T15:59:24.825285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
112118
28.2%
210870
25.3%
35437
12.7%
42950
 
6.9%
02709
 
6.3%
51193
 
2.8%
6481
 
1.1%
7158
 
0.4%
846
 
0.1%
922
 
0.1%
Other values (4)9
 
< 0.1%
(Missing)6969
16.2%
ValueCountFrequency (%)
02709
 
6.3%
112118
28.2%
210870
25.3%
35437
12.7%
42950
 
6.9%
51193
 
2.8%
6481
 
1.1%
7158
 
0.4%
846
 
0.1%
922
 
0.1%
ValueCountFrequency (%)
241
 
< 0.1%
131
 
< 0.1%
112
 
< 0.1%
105
 
< 0.1%
922
 
0.1%
846
 
0.1%
7158
 
0.4%
6481
 
1.1%
51193
2.8%
42950
6.9%

ANZ_STATISTISCHE_HAUSHALTE
Real number (ℝ≥0)

MISSING

Distinct173
Distinct (%)0.5%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean6.275856189
Minimum0
Maximum369
Zeros95
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:24.945312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q37
95-th percentile24
Maximum369
Range369
Interquartile range (IQR)6

Descriptive statistics

Standard deviation14.32633307
Coefficient of variation (CV)2.282769497
Kurtosis148.1784167
Mean6.275856189
Median Absolute Deviation (MAD)1
Skewness9.519613815
Sum220816
Variance205.2438193
MonotonicityNot monotonic
2021-12-29T15:59:25.068810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
114784
34.4%
25391
 
12.5%
32107
 
4.9%
41418
 
3.3%
61311
 
3.1%
51280
 
3.0%
71177
 
2.7%
81053
 
2.5%
9918
 
2.1%
10753
 
1.8%
Other values (163)4993
 
11.6%
(Missing)7777
18.1%
ValueCountFrequency (%)
095
 
0.2%
114784
34.4%
25391
 
12.5%
32107
 
4.9%
41418
 
3.3%
51280
 
3.0%
61311
 
3.1%
71177
 
2.7%
81053
 
2.5%
9918
 
2.1%
ValueCountFrequency (%)
3691
 
< 0.1%
3541
 
< 0.1%
3422
< 0.1%
3392
< 0.1%
3221
 
< 0.1%
3191
 
< 0.1%
3043
< 0.1%
2991
 
< 0.1%
2971
 
< 0.1%
2741
 
< 0.1%

ANZ_TITEL
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Memory size335.8 KiB
0.0
35674 
1.0
 
293
2.0
 
26

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035674
83.0%
1.0293
 
0.7%
2.026
 
0.1%
(Missing)6969
 
16.2%

Length

2021-12-29T15:59:25.180596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:25.251865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035674
99.1%
1.0293
 
0.8%
2.026
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ARBEIT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7951
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.045214361
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:25.312962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.036404283
Coefficient of variation (CV)0.3403386955
Kurtosis-0.4478248745
Mean3.045214361
Median Absolute Deviation (MAD)1
Skewness-0.3506897956
Sum106616
Variance1.074133838
MonotonicityNot monotonic
2021-12-29T15:59:25.391967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
412323
28.7%
311223
26.1%
26910
16.1%
13242
 
7.5%
51306
 
3.0%
97
 
< 0.1%
(Missing)7951
18.5%
ValueCountFrequency (%)
13242
 
7.5%
26910
16.1%
311223
26.1%
412323
28.7%
51306
 
3.0%
97
 
< 0.1%
ValueCountFrequency (%)
97
 
< 0.1%
51306
 
3.0%
412323
28.7%
311223
26.1%
26910
16.1%
13242
 
7.5%

BALLRAUM
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing7799
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean4.256775588
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:25.483747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.195305614
Coefficient of variation (CV)0.5157203071
Kurtosis-1.483222281
Mean4.256775588
Median Absolute Deviation (MAD)2
Skewness-0.3155678814
Sum149681
Variance4.819366737
MonotonicityNot monotonic
2021-12-29T15:59:25.562102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
611579
27.0%
16521
15.2%
74905
11.4%
24329
 
10.1%
32967
 
6.9%
42518
 
5.9%
52344
 
5.5%
(Missing)7799
18.2%
ValueCountFrequency (%)
16521
15.2%
24329
 
10.1%
32967
 
6.9%
42518
 
5.9%
52344
 
5.5%
611579
27.0%
74905
11.4%
ValueCountFrequency (%)
74905
11.4%
611579
27.0%
52344
 
5.5%
42518
 
5.9%
32967
 
6.9%
24329
 
10.1%
16521
15.2%

CAMEO_DEU_2015
Categorical

MISSING

Distinct45
Distinct (%)0.1%
Missing7888
Missing (%)18.4%
Memory size335.8 KiB
6B
 
2452
4C
 
2216
3D
 
2152
2D
 
1991
4A
 
1684
Other values (40)
24579 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5D
2nd row5B
3rd row2D
4th row2D
5th row7B

Common Values

ValueCountFrequency (%)
6B2452
 
5.7%
4C2216
 
5.2%
3D2152
 
5.0%
2D1991
 
4.6%
4A1684
 
3.9%
8A1597
 
3.7%
3C1547
 
3.6%
8C1267
 
2.9%
7A1242
 
2.9%
2C1173
 
2.7%
Other values (35)17753
41.3%
(Missing)7888
18.4%

Length

2021-12-29T15:59:25.658946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6b2452
 
7.0%
4c2216
 
6.3%
3d2152
 
6.1%
2d1991
 
5.7%
4a1684
 
4.8%
8a1597
 
4.6%
3c1547
 
4.4%
8c1267
 
3.6%
7a1242
 
3.5%
2c1173
 
3.3%
Other values (35)17753
50.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_DEUG_2015
Categorical

MISSING

Distinct10
Distinct (%)< 0.1%
Missing7888
Missing (%)18.4%
Memory size335.8 KiB
6
5363 
4
5131 
8
5010 
2
4703 
3
4423 
Other values (5)
10444 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row2
4th row2
5th row7

Common Values

ValueCountFrequency (%)
65363
12.5%
45131
11.9%
85010
11.7%
24703
10.9%
34423
10.3%
73064
 
7.1%
52531
 
5.9%
92460
 
5.7%
12378
 
5.5%
X11
 
< 0.1%
(Missing)7888
18.4%

Length

2021-12-29T15:59:25.759107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:25.843277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
65363
15.3%
45131
14.6%
85010
14.3%
24703
13.4%
34423
12.6%
73064
8.7%
52531
7.2%
92460
7.0%
12378
6.8%
x11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CAMEO_INTL_2015
Categorical

MISSING

Distinct22
Distinct (%)0.1%
Missing7888
Missing (%)18.4%
Memory size335.8 KiB
24
4210 
14
3674 
51
3214 
41
3064 
25
2455 
Other values (17)
18457 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34
2nd row32
3rd row14
4th row14
5th row41

Common Values

ValueCountFrequency (%)
244210
9.8%
143674
8.6%
513214
 
7.5%
413064
 
7.1%
252455
 
5.7%
432452
 
5.7%
451906
 
4.4%
541856
 
4.3%
221684
 
3.9%
131633
 
3.8%
Other values (12)8926
20.8%
(Missing)7888
18.4%

Length

2021-12-29T15:59:25.976237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
244210
12.0%
143674
10.5%
513214
 
9.2%
413064
 
8.7%
252455
 
7.0%
432452
 
7.0%
451906
 
5.4%
541856
 
5.3%
221684
 
4.8%
131633
 
4.7%
Other values (12)8926
25.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_GESAMTTYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean3.314233775
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:26.057994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.775396833
Coefficient of variation (CV)0.5356884738
Kurtosis-1.330734723
Mean3.314233775
Median Absolute Deviation (MAD)1
Skewness0.3074453671
Sum140381
Variance3.152033914
MonotonicityNot monotonic
2021-12-29T15:59:26.147743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
212813
29.8%
68429
19.6%
16671
15.5%
46024
14.0%
34343
 
10.1%
54077
 
9.5%
(Missing)605
 
1.4%
ValueCountFrequency (%)
16671
15.5%
212813
29.8%
34343
 
10.1%
46024
14.0%
54077
 
9.5%
68429
19.6%
ValueCountFrequency (%)
68429
19.6%
54077
 
9.5%
46024
14.0%
34343
 
10.1%
212813
29.8%
16671
15.5%

CJT_KATALOGNUTZER
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
22813 
4.0
6049 
1.0
5558 
3.0
5340 
2.0
2597 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.022813
53.1%
4.06049
 
14.1%
1.05558
 
12.9%
3.05340
 
12.4%
2.02597
 
6.0%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:26.250128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:26.313822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.022813
53.9%
4.06049
 
14.3%
1.05558
 
13.1%
3.05340
 
12.6%
2.02597
 
6.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
2.0
17526 
1.0
9424 
5.0
7243 
3.0
5717 
4.0
2447 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.017526
40.8%
1.09424
21.9%
5.07243
16.9%
3.05717
 
13.3%
4.02447
 
5.7%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:26.405465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:26.476394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.017526
41.4%
1.09424
22.2%
5.07243
17.1%
3.05717
 
13.5%
4.02447
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
1.0
14956 
2.0
14303 
5.0
6559 
3.0
4520 
4.0
2019 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.014956
34.8%
2.014303
33.3%
5.06559
15.3%
3.04520
 
10.5%
4.02019
 
4.7%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:26.577754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:26.648760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.014956
35.3%
2.014303
33.8%
5.06559
15.5%
3.04520
 
10.7%
4.02019
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_3
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
29351 
4.0
6718 
3.0
3824 
2.0
 
1903
1.0
 
561

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.029351
68.3%
4.06718
 
15.6%
3.03824
 
8.9%
2.01903
 
4.4%
1.0561
 
1.3%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:26.739838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:26.821484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.029351
69.3%
4.06718
 
15.9%
3.03824
 
9.0%
2.01903
 
4.5%
1.0561
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_4
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
28461 
4.0
6670 
3.0
3180 
2.0
 
2832
1.0
 
1214

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.028461
66.2%
4.06670
 
15.5%
3.03180
 
7.4%
2.02832
 
6.6%
1.01214
 
2.8%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:26.912790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:27.004624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.028461
67.2%
4.06670
 
15.7%
3.03180
 
7.5%
2.02832
 
6.7%
1.01214
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_5
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
29640 
4.0
5738 
3.0
4750 
2.0
 
1493
1.0
 
736

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.029640
69.0%
4.05738
 
13.4%
3.04750
 
11.1%
2.01493
 
3.5%
1.0736
 
1.7%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:27.104141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:27.177715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.029640
70.0%
4.05738
 
13.5%
3.04750
 
11.2%
2.01493
 
3.5%
1.0736
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CJT_TYP_6
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
28543 
4.0
7198 
3.0
3381 
2.0
 
2527
1.0
 
708

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.028543
66.4%
4.07198
 
16.8%
3.03381
 
7.9%
2.02527
 
5.9%
1.0708
 
1.6%
(Missing)605
 
1.4%

Length

2021-12-29T15:59:27.390827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:27.471790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.028543
67.4%
4.07198
 
17.0%
3.03381
 
8.0%
2.02527
 
6.0%
1.0708
 
1.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_BANKEN_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1017177971
Minimum0
Maximum6
Zeros40198
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:27.553680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4540652053
Coefficient of variation (CV)4.463970103
Kurtosis43.06699646
Mean0.1017177971
Median Absolute Deviation (MAD)0
Skewness5.920366395
Sum4370
Variance0.2061752107
MonotonicityNot monotonic
2021-12-29T15:59:27.633732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
040198
93.6%
11726
 
4.0%
2693
 
1.6%
3180
 
0.4%
4118
 
0.3%
536
 
0.1%
611
 
< 0.1%
ValueCountFrequency (%)
040198
93.6%
11726
 
4.0%
2693
 
1.6%
3180
 
0.4%
4118
 
0.3%
536
 
0.1%
611
 
< 0.1%
ValueCountFrequency (%)
611
 
< 0.1%
536
 
0.1%
4118
 
0.3%
3180
 
0.4%
2693
 
1.6%
11726
 
4.0%
040198
93.6%

D19_BANKEN_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1760625669
Minimum0
Maximum6
Zeros38714
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:27.724931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6328577229
Coefficient of variation (CV)3.594504692
Kurtosis25.07695609
Mean0.1760625669
Median Absolute Deviation (MAD)0
Skewness4.635247071
Sum7564
Variance0.4005088975
MonotonicityNot monotonic
2021-12-29T15:59:27.795888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
038714
90.1%
12304
 
5.4%
21162
 
2.7%
3362
 
0.8%
4289
 
0.7%
592
 
0.2%
639
 
0.1%
ValueCountFrequency (%)
038714
90.1%
12304
 
5.4%
21162
 
2.7%
3362
 
0.8%
4289
 
0.7%
592
 
0.2%
639
 
0.1%
ValueCountFrequency (%)
639
 
0.1%
592
 
0.2%
4289
 
0.7%
3362
 
0.8%
21162
 
2.7%
12304
 
5.4%
038714
90.1%

D19_BANKEN_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.352776873
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:27.897962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.630258688
Coefficient of variation (CV)0.1743074501
Kurtosis9.031150149
Mean9.352776873
Median Absolute Deviation (MAD)0
Skewness-3.002569089
Sum401814
Variance2.657743389
MonotonicityNot monotonic
2021-12-29T15:59:27.978007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1033913
78.9%
93504
 
8.2%
51706
 
4.0%
81297
 
3.0%
7805
 
1.9%
6679
 
1.6%
1342
 
0.8%
4292
 
0.7%
2249
 
0.6%
3175
 
0.4%
ValueCountFrequency (%)
1342
 
0.8%
2249
 
0.6%
3175
 
0.4%
4292
 
0.7%
51706
 
4.0%
6679
 
1.6%
7805
 
1.9%
81297
 
3.0%
93504
 
8.2%
1033913
78.9%
ValueCountFrequency (%)
1033913
78.9%
93504
 
8.2%
81297
 
3.0%
7805
 
1.9%
6679
 
1.6%
51706
 
4.0%
4292
 
0.7%
3175
 
0.4%
2249
 
0.6%
1342
 
0.8%

D19_BANKEN_DIREKT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7028304083
Minimum0
Maximum7
Zeros36805
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.062298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.818202309
Coefficient of variation (CV)2.586971604
Kurtosis4.007649091
Mean0.7028304083
Median Absolute Deviation (MAD)0
Skewness2.381729213
Sum30195
Variance3.305859636
MonotonicityNot monotonic
2021-12-29T15:59:28.143810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
036805
85.7%
63119
 
7.3%
31232
 
2.9%
5730
 
1.7%
7323
 
0.8%
2281
 
0.7%
4280
 
0.7%
1192
 
0.4%
ValueCountFrequency (%)
036805
85.7%
1192
 
0.4%
2281
 
0.7%
31232
 
2.9%
4280
 
0.7%
5730
 
1.7%
63119
 
7.3%
7323
 
0.8%
ValueCountFrequency (%)
7323
 
0.8%
63119
 
7.3%
5730
 
1.7%
4280
 
0.7%
31232
 
2.9%
2281
 
0.7%
1192
 
0.4%
036805
85.7%

D19_BANKEN_GROSS
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4633629719
Minimum0
Maximum6
Zeros38760
Zeros (%)90.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.233671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.488132905
Coefficient of variation (CV)3.211592197
Kurtosis8.087930639
Mean0.4633629719
Median Absolute Deviation (MAD)0
Skewness3.106387172
Sum19907
Variance2.214539543
MonotonicityNot monotonic
2021-12-29T15:59:28.316864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
038760
90.2%
62138
 
5.0%
3853
 
2.0%
5586
 
1.4%
4258
 
0.6%
2191
 
0.4%
1176
 
0.4%
ValueCountFrequency (%)
038760
90.2%
1176
 
0.4%
2191
 
0.4%
3853
 
2.0%
4258
 
0.6%
5586
 
1.4%
62138
 
5.0%
ValueCountFrequency (%)
62138
 
5.0%
5586
 
1.4%
4258
 
0.6%
3853
 
2.0%
2191
 
0.4%
1176
 
0.4%
038760
90.2%

D19_BANKEN_LOKAL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1043945813
Minimum0
Maximum7
Zeros42170
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.417243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7940971665
Coefficient of variation (CV)7.606689514
Kurtosis61.27084897
Mean0.1043945813
Median Absolute Deviation (MAD)0
Skewness7.845757537
Sum4485
Variance0.6305903099
MonotonicityNot monotonic
2021-12-29T15:59:28.499398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
042170
98.2%
7367
 
0.9%
6195
 
0.5%
3182
 
0.4%
532
 
0.1%
29
 
< 0.1%
45
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
042170
98.2%
12
 
< 0.1%
29
 
< 0.1%
3182
 
0.4%
45
 
< 0.1%
532
 
0.1%
6195
 
0.5%
7367
 
0.9%
ValueCountFrequency (%)
7367
 
0.9%
6195
 
0.5%
532
 
0.1%
45
 
< 0.1%
3182
 
0.4%
29
 
< 0.1%
12
 
< 0.1%
042170
98.2%

D19_BANKEN_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.851799264
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.593472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8212257993
Coefficient of variation (CV)0.08335795089
Kurtosis39.03040409
Mean9.851799264
Median Absolute Deviation (MAD)0
Skewness-6.118856861
Sum423253
Variance0.6744118134
MonotonicityNot monotonic
2021-12-29T15:59:28.674923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1041258
96.0%
5849
 
2.0%
8443
 
1.0%
9250
 
0.6%
661
 
0.1%
255
 
0.1%
416
 
< 0.1%
114
 
< 0.1%
78
 
< 0.1%
38
 
< 0.1%
ValueCountFrequency (%)
114
 
< 0.1%
255
 
0.1%
38
 
< 0.1%
416
 
< 0.1%
5849
 
2.0%
661
 
0.1%
78
 
< 0.1%
8443
 
1.0%
9250
 
0.6%
1041258
96.0%
ValueCountFrequency (%)
1041258
96.0%
9250
 
0.6%
8443
 
1.0%
78
 
< 0.1%
661
 
0.1%
5849
 
2.0%
416
 
< 0.1%
38
 
< 0.1%
255
 
0.1%
114
 
< 0.1%

D19_BANKEN_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.579698338
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.764777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.359768472
Coefficient of variation (CV)0.1419427234
Kurtosis17.49548611
Mean9.579698338
Median Absolute Deviation (MAD)0
Skewness-4.051365088
Sum411563
Variance1.848970296
MonotonicityNot monotonic
2021-12-29T15:59:28.848645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1036915
85.9%
92561
 
6.0%
5787
 
1.8%
8762
 
1.8%
7628
 
1.5%
6505
 
1.2%
1290
 
0.7%
4228
 
0.5%
2153
 
0.4%
3133
 
0.3%
ValueCountFrequency (%)
1290
 
0.7%
2153
 
0.4%
3133
 
0.3%
4228
 
0.5%
5787
 
1.8%
6505
 
1.2%
7628
 
1.5%
8762
 
1.8%
92561
 
6.0%
1036915
85.9%
ValueCountFrequency (%)
1036915
85.9%
92561
 
6.0%
8762
 
1.8%
7628
 
1.5%
6505
 
1.2%
5787
 
1.8%
4228
 
0.5%
3133
 
0.3%
2153
 
0.4%
1290
 
0.7%

D19_BANKEN_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean0.4374187348
Minimum0
Maximum10
Zeros33787
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:28.938215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.029657884
Coefficient of variation (CV)4.64007991
Kurtosis17.93882446
Mean0.4374187348
Median Absolute Deviation (MAD)0
Skewness4.456240551
Sum15475
Variance4.119511125
MonotonicityNot monotonic
2021-12-29T15:59:29.032581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
033787
78.6%
101483
 
3.5%
542
 
0.1%
725
 
0.1%
818
 
< 0.1%
314
 
< 0.1%
98
 
< 0.1%
21
 
< 0.1%
(Missing)7584
 
17.7%
ValueCountFrequency (%)
033787
78.6%
21
 
< 0.1%
314
 
< 0.1%
542
 
0.1%
725
 
0.1%
818
 
< 0.1%
98
 
< 0.1%
101483
 
3.5%
ValueCountFrequency (%)
101483
 
3.5%
98
 
< 0.1%
818
 
< 0.1%
725
 
0.1%
542
 
0.1%
314
 
< 0.1%
21
 
< 0.1%
033787
78.6%

D19_BANKEN_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3760532564
Minimum0
Maximum7
Zeros39983
Zeros (%)93.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.113653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.416897856
Coefficient of variation (CV)3.767811692
Kurtosis11.42658949
Mean0.3760532564
Median Absolute Deviation (MAD)0
Skewness3.623033168
Sum16156
Variance2.007599534
MonotonicityNot monotonic
2021-12-29T15:59:29.195481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
039983
93.1%
61956
 
4.6%
5357
 
0.8%
3340
 
0.8%
7184
 
0.4%
280
 
0.2%
435
 
0.1%
127
 
0.1%
ValueCountFrequency (%)
039983
93.1%
127
 
0.1%
280
 
0.2%
3340
 
0.8%
435
 
0.1%
5357
 
0.8%
61956
 
4.6%
7184
 
0.4%
ValueCountFrequency (%)
7184
 
0.4%
61956
 
4.6%
5357
 
0.8%
435
 
0.1%
3340
 
0.8%
280
 
0.2%
127
 
0.1%
039983
93.1%

D19_BEKLEIDUNG_GEH
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8254736744
Minimum0
Maximum7
Zeros35949
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.287220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.962660932
Coefficient of variation (CV)2.377617836
Kurtosis2.752862429
Mean0.8254736744
Median Absolute Deviation (MAD)0
Skewness2.113969372
Sum35464
Variance3.852037932
MonotonicityNot monotonic
2021-12-29T15:59:29.369066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035949
83.7%
63496
 
8.1%
31442
 
3.4%
51065
 
2.5%
7532
 
1.2%
2209
 
0.5%
4142
 
0.3%
1127
 
0.3%
ValueCountFrequency (%)
035949
83.7%
1127
 
0.3%
2209
 
0.5%
31442
 
3.4%
4142
 
0.3%
51065
 
2.5%
63496
 
8.1%
7532
 
1.2%
ValueCountFrequency (%)
7532
 
1.2%
63496
 
8.1%
51065
 
2.5%
4142
 
0.3%
31442
 
3.4%
2209
 
0.5%
1127
 
0.3%
035949
83.7%

D19_BEKLEIDUNG_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.655113822
Minimum0
Maximum7
Zeros29981
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.461337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.634217798
Coefficient of variation (CV)1.591562927
Kurtosis-0.7262049921
Mean1.655113822
Median Absolute Deviation (MAD)0
Skewness1.065971234
Sum71107
Variance6.939103408
MonotonicityNot monotonic
2021-12-29T15:59:29.532831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
029981
69.8%
67690
 
17.9%
71975
 
4.6%
31645
 
3.8%
5921
 
2.1%
2327
 
0.8%
1248
 
0.6%
4175
 
0.4%
ValueCountFrequency (%)
029981
69.8%
1248
 
0.6%
2327
 
0.8%
31645
 
3.8%
4175
 
0.4%
5921
 
2.1%
67690
 
17.9%
71975
 
4.6%
ValueCountFrequency (%)
71975
 
4.6%
67690
 
17.9%
5921
 
2.1%
4175
 
0.4%
31645
 
3.8%
2327
 
0.8%
1248
 
0.6%
029981
69.8%

D19_BILDUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.017201248
Minimum0
Maximum7
Zeros34895
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.624525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.23234489
Coefficient of variation (CV)2.194595116
Kurtosis1.687023278
Mean1.017201248
Median Absolute Deviation (MAD)0
Skewness1.870464198
Sum43701
Variance4.983363708
MonotonicityNot monotonic
2021-12-29T15:59:29.705673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
034895
81.2%
64249
 
9.9%
71741
 
4.1%
2915
 
2.1%
3584
 
1.4%
5369
 
0.9%
4128
 
0.3%
181
 
0.2%
ValueCountFrequency (%)
034895
81.2%
181
 
0.2%
2915
 
2.1%
3584
 
1.4%
4128
 
0.3%
5369
 
0.9%
64249
 
9.9%
71741
 
4.1%
ValueCountFrequency (%)
71741
 
4.1%
64249
 
9.9%
5369
 
0.9%
4128
 
0.3%
3584
 
1.4%
2915
 
2.1%
181
 
0.2%
034895
81.2%

D19_BIO_OEKO
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63658582
Minimum0
Maximum7
Zeros38422
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.797893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.875460819
Coefficient of variation (CV)2.946124089
Kurtosis5.224113502
Mean0.63658582
Median Absolute Deviation (MAD)0
Skewness2.66208952
Sum27349
Variance3.517353283
MonotonicityNot monotonic
2021-12-29T15:59:29.879714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
038422
89.4%
62869
 
6.7%
71152
 
2.7%
5255
 
0.6%
3252
 
0.6%
48
 
< 0.1%
24
 
< 0.1%
ValueCountFrequency (%)
038422
89.4%
24
 
< 0.1%
3252
 
0.6%
48
 
< 0.1%
5255
 
0.6%
62869
 
6.7%
71152
 
2.7%
ValueCountFrequency (%)
71152
 
2.7%
62869
 
6.7%
5255
 
0.6%
48
 
< 0.1%
3252
 
0.6%
24
 
< 0.1%
038422
89.4%

D19_BUCH_CD
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.526721289
Minimum0
Maximum7
Zeros22410
Zeros (%)52.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:29.980769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.837579407
Coefficient of variation (CV)1.123028258
Kurtosis-1.77898207
Mean2.526721289
Median Absolute Deviation (MAD)0
Skewness0.3414315952
Sum108553
Variance8.051856893
MonotonicityNot monotonic
2021-12-29T15:59:30.059906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
022410
52.2%
614592
34.0%
31727
 
4.0%
51132
 
2.6%
11054
 
2.5%
7782
 
1.8%
2714
 
1.7%
4551
 
1.3%
ValueCountFrequency (%)
022410
52.2%
11054
 
2.5%
2714
 
1.7%
31727
 
4.0%
4551
 
1.3%
51132
 
2.6%
614592
34.0%
7782
 
1.8%
ValueCountFrequency (%)
7782
 
1.8%
614592
34.0%
51132
 
2.6%
4551
 
1.3%
31727
 
4.0%
2714
 
1.7%
11054
 
2.5%
022410
52.2%

D19_DIGIT_SERV
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1995018854
Minimum0
Maximum7
Zeros41226
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:30.143316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.019639885
Coefficient of variation (CV)5.110928566
Kurtosis26.23316305
Mean0.1995018854
Median Absolute Deviation (MAD)0
Skewness5.216089215
Sum8571
Variance1.039665495
MonotonicityNot monotonic
2021-12-29T15:59:30.225098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
041226
96.0%
6890
 
2.1%
3431
 
1.0%
5189
 
0.4%
7101
 
0.2%
274
 
0.2%
429
 
0.1%
122
 
0.1%
ValueCountFrequency (%)
041226
96.0%
122
 
0.1%
274
 
0.2%
3431
 
1.0%
429
 
0.1%
5189
 
0.4%
6890
 
2.1%
7101
 
0.2%
ValueCountFrequency (%)
7101
 
0.2%
6890
 
2.1%
5189
 
0.4%
429
 
0.1%
3431
 
1.0%
274
 
0.2%
122
 
0.1%
041226
96.0%

D19_DROGERIEARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6809273311
Minimum0
Maximum7
Zeros36725
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:30.317125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.769576723
Coefficient of variation (CV)2.598774704
Kurtosis4.396707696
Mean0.6809273311
Median Absolute Deviation (MAD)0
Skewness2.440765157
Sum29254
Variance3.13140178
MonotonicityNot monotonic
2021-12-29T15:59:30.398377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
036725
85.5%
62506
 
5.8%
31272
 
3.0%
5885
 
2.1%
4446
 
1.0%
7440
 
1.0%
2425
 
1.0%
1263
 
0.6%
ValueCountFrequency (%)
036725
85.5%
1263
 
0.6%
2425
 
1.0%
31272
 
3.0%
4446
 
1.0%
5885
 
2.1%
62506
 
5.8%
7440
 
1.0%
ValueCountFrequency (%)
7440
 
1.0%
62506
 
5.8%
5885
 
2.1%
4446
 
1.0%
31272
 
3.0%
2425
 
1.0%
1263
 
0.6%
036725
85.5%

D19_ENERGIE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4239095014
Minimum0
Maximum7
Zeros39064
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:30.499913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.427791147
Coefficient of variation (CV)3.368150849
Kurtosis10.14904865
Mean0.4239095014
Median Absolute Deviation (MAD)0
Skewness3.371500035
Sum18212
Variance2.03858756
MonotonicityNot monotonic
2021-12-29T15:59:30.591320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
039064
90.9%
61326
 
3.1%
31244
 
2.9%
5597
 
1.4%
7391
 
0.9%
2183
 
0.4%
493
 
0.2%
164
 
0.1%
ValueCountFrequency (%)
039064
90.9%
164
 
0.1%
2183
 
0.4%
31244
 
2.9%
493
 
0.2%
5597
 
1.4%
61326
 
3.1%
7391
 
0.9%
ValueCountFrequency (%)
7391
 
0.9%
61326
 
3.1%
5597
 
1.4%
493
 
0.2%
31244
 
2.9%
2183
 
0.4%
164
 
0.1%
039064
90.9%

D19_FREIZEIT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.614566361
Minimum0
Maximum7
Zeros37678
Zeros (%)87.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:30.691570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.718502466
Coefficient of variation (CV)2.796284624
Kurtosis5.160339918
Mean0.614566361
Median Absolute Deviation (MAD)0
Skewness2.610124421
Sum26403
Variance2.953250724
MonotonicityNot monotonic
2021-12-29T15:59:30.764930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
037678
87.7%
62757
 
6.4%
31131
 
2.6%
5743
 
1.7%
7241
 
0.6%
2198
 
0.5%
4152
 
0.4%
162
 
0.1%
ValueCountFrequency (%)
037678
87.7%
162
 
0.1%
2198
 
0.5%
31131
 
2.6%
4152
 
0.4%
5743
 
1.7%
62757
 
6.4%
7241
 
0.6%
ValueCountFrequency (%)
7241
 
0.6%
62757
 
6.4%
5743
 
1.7%
4152
 
0.4%
31131
 
2.6%
2198
 
0.5%
162
 
0.1%
037678
87.7%

D19_GARTEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4072436106
Minimum0
Maximum7
Zeros39737
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:30.856077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.468958035
Coefficient of variation (CV)3.60707448
Kurtosis10.30109182
Mean0.4072436106
Median Absolute Deviation (MAD)0
Skewness3.458731118
Sum17496
Variance2.157837708
MonotonicityNot monotonic
2021-12-29T15:59:30.937136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
039737
92.5%
61887
 
4.4%
5492
 
1.1%
3450
 
1.0%
7300
 
0.7%
245
 
0.1%
441
 
0.1%
110
 
< 0.1%
ValueCountFrequency (%)
039737
92.5%
110
 
< 0.1%
245
 
0.1%
3450
 
1.0%
441
 
0.1%
5492
 
1.1%
61887
 
4.4%
7300
 
0.7%
ValueCountFrequency (%)
7300
 
0.7%
61887
 
4.4%
5492
 
1.1%
441
 
0.1%
3450
 
1.0%
245
 
0.1%
110
 
< 0.1%
039737
92.5%

D19_GESAMT_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9556119361
Minimum0
Maximum6
Zeros25341
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:31.171251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.42541379
Coefficient of variation (CV)1.491624096
Kurtosis1.222362458
Mean0.9556119361
Median Absolute Deviation (MAD)0
Skewness1.466870058
Sum41055
Variance2.031804473
MonotonicityNot monotonic
2021-12-29T15:59:31.250510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
025341
59.0%
16096
 
14.2%
25145
 
12.0%
42548
 
5.9%
32465
 
5.7%
51120
 
2.6%
6247
 
0.6%
ValueCountFrequency (%)
025341
59.0%
16096
 
14.2%
25145
 
12.0%
32465
 
5.7%
42548
 
5.9%
51120
 
2.6%
6247
 
0.6%
ValueCountFrequency (%)
6247
 
0.6%
51120
 
2.6%
42548
 
5.9%
32465
 
5.7%
25145
 
12.0%
16096
 
14.2%
025341
59.0%

D19_GESAMT_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.445323774
Minimum0
Maximum6
Zeros20736
Zeros (%)48.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:31.333598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.771810917
Coefficient of variation (CV)1.225892045
Kurtosis-0.2367616882
Mean1.445323774
Median Absolute Deviation (MAD)1
Skewness0.987304502
Sum62094
Variance3.139313925
MonotonicityNot monotonic
2021-12-29T15:59:31.415003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
020736
48.3%
26026
 
14.0%
15449
 
12.7%
43835
 
8.9%
33233
 
7.5%
52544
 
5.9%
61139
 
2.7%
ValueCountFrequency (%)
020736
48.3%
15449
 
12.7%
26026
 
14.0%
33233
 
7.5%
43835
 
8.9%
52544
 
5.9%
61139
 
2.7%
ValueCountFrequency (%)
61139
 
2.7%
52544
 
5.9%
43835
 
8.9%
33233
 
7.5%
26026
 
14.0%
15449
 
12.7%
020736
48.3%

D19_GESAMT_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.547530376
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:31.517348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.170354825
Coefficient of variation (CV)0.4842062034
Kurtosis-1.25728655
Mean6.547530376
Median Absolute Deviation (MAD)3
Skewness-0.4031662872
Sum281295
Variance10.05114972
MonotonicityNot monotonic
2021-12-29T15:59:31.598677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1012031
28.0%
55846
13.6%
95829
13.6%
13944
 
9.2%
23386
 
7.9%
82876
 
6.7%
62501
 
5.8%
42501
 
5.8%
72104
 
4.9%
31944
 
4.5%
ValueCountFrequency (%)
13944
 
9.2%
23386
 
7.9%
31944
 
4.5%
42501
 
5.8%
55846
13.6%
62501
 
5.8%
72104
 
4.9%
82876
 
6.7%
95829
13.6%
1012031
28.0%
ValueCountFrequency (%)
1012031
28.0%
95829
13.6%
82876
 
6.7%
72104
 
4.9%
62501
 
5.8%
55846
13.6%
42501
 
5.8%
31944
 
4.5%
23386
 
7.9%
13944
 
9.2%

D19_GESAMT_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.237349285
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:31.690034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.292575121
Coefficient of variation (CV)0.2783146667
Kurtosis1.112848068
Mean8.237349285
Median Absolute Deviation (MAD)1
Skewness-1.404995523
Sum353893
Variance5.255900685
MonotonicityNot monotonic
2021-12-29T15:59:31.771534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1018064
42.0%
99532
22.2%
84257
 
9.9%
54001
 
9.3%
72042
 
4.8%
61791
 
4.2%
2999
 
2.3%
4936
 
2.2%
1699
 
1.6%
3641
 
1.5%
ValueCountFrequency (%)
1699
 
1.6%
2999
 
2.3%
3641
 
1.5%
4936
 
2.2%
54001
 
9.3%
61791
 
4.2%
72042
 
4.8%
84257
 
9.9%
99532
22.2%
1018064
42.0%
ValueCountFrequency (%)
1018064
42.0%
99532
22.2%
84257
 
9.9%
72042
 
4.8%
61791
 
4.2%
54001
 
9.3%
4936
 
2.2%
3641
 
1.5%
2999
 
2.3%
1699
 
1.6%

D19_GESAMT_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.563404869
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:31.863084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.024221891
Coefficient of variation (CV)0.3998492667
Kurtosis-0.564029077
Mean7.563404869
Median Absolute Deviation (MAD)1
Skewness-0.9277928841
Sum324939
Variance9.145918045
MonotonicityNot monotonic
2021-12-29T15:59:31.944796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1019871
46.3%
95038
 
11.7%
54110
 
9.6%
12674
 
6.2%
82249
 
5.2%
22054
 
4.8%
62006
 
4.7%
41839
 
4.3%
71702
 
4.0%
31419
 
3.3%
ValueCountFrequency (%)
12674
 
6.2%
22054
 
4.8%
31419
 
3.3%
41839
 
4.3%
54110
 
9.6%
62006
 
4.7%
71702
 
4.0%
82249
 
5.2%
95038
 
11.7%
1019871
46.3%
ValueCountFrequency (%)
1019871
46.3%
95038
 
11.7%
82249
 
5.2%
71702
 
4.0%
62006
 
4.7%
54110
 
9.6%
41839
 
4.3%
31419
 
3.3%
22054
 
4.8%
12674
 
6.2%

D19_GESAMT_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.90202951
Minimum0
Maximum10
Zeros23291
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.036742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.297059574
Coefficient of variation (CV)1.480708435
Kurtosis-1.053842881
Mean2.90202951
Median Absolute Deviation (MAD)0
Skewness0.9127195684
Sum102668
Variance18.46472098
MonotonicityNot monotonic
2021-12-29T15:59:32.127448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
023291
54.2%
108171
 
19.0%
51009
 
2.3%
8629
 
1.5%
7531
 
1.2%
3520
 
1.2%
9337
 
0.8%
1283
 
0.7%
2250
 
0.6%
6180
 
0.4%
(Missing)7584
 
17.7%
ValueCountFrequency (%)
023291
54.2%
1283
 
0.7%
2250
 
0.6%
3520
 
1.2%
4177
 
0.4%
51009
 
2.3%
6180
 
0.4%
7531
 
1.2%
8629
 
1.5%
9337
 
0.8%
ValueCountFrequency (%)
108171
19.0%
9337
 
0.8%
8629
 
1.5%
7531
 
1.2%
6180
 
0.4%
51009
 
2.3%
4177
 
0.4%
3520
 
1.2%
2250
 
0.6%
1283
 
0.7%

D19_HANDWERK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.687374889
Minimum0
Maximum7
Zeros31086
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.217509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.746567981
Coefficient of variation (CV)1.627716519
Kurtosis-0.8870511223
Mean1.687374889
Median Absolute Deviation (MAD)0
Skewness1.034615326
Sum72493
Variance7.543635673
MonotonicityNot monotonic
2021-12-29T15:59:32.299613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
031086
72.4%
69378
 
21.8%
72057
 
4.8%
5250
 
0.6%
3180
 
0.4%
48
 
< 0.1%
12
 
< 0.1%
21
 
< 0.1%
ValueCountFrequency (%)
031086
72.4%
12
 
< 0.1%
21
 
< 0.1%
3180
 
0.4%
48
 
< 0.1%
5250
 
0.6%
69378
 
21.8%
72057
 
4.8%
ValueCountFrequency (%)
72057
 
4.8%
69378
 
21.8%
5250
 
0.6%
48
 
< 0.1%
3180
 
0.4%
21
 
< 0.1%
12
 
< 0.1%
031086
72.4%

D19_HAUS_DEKO
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.624249337
Minimum0
Maximum7
Zeros28556
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.383534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.475151796
Coefficient of variation (CV)1.523874285
Kurtosis-0.7451399847
Mean1.624249337
Median Absolute Deviation (MAD)0
Skewness1.030271963
Sum69781
Variance6.126376412
MonotonicityNot monotonic
2021-12-29T15:59:32.484681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
028556
66.5%
68004
 
18.6%
32714
 
6.3%
51526
 
3.6%
2790
 
1.8%
1701
 
1.6%
7340
 
0.8%
4331
 
0.8%
ValueCountFrequency (%)
028556
66.5%
1701
 
1.6%
2790
 
1.8%
32714
 
6.3%
4331
 
0.8%
51526
 
3.6%
68004
 
18.6%
7340
 
0.8%
ValueCountFrequency (%)
7340
 
0.8%
68004
 
18.6%
51526
 
3.6%
4331
 
0.8%
32714
 
6.3%
2790
 
1.8%
1701
 
1.6%
028556
66.5%

D19_KINDERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.168427913
Minimum0
Maximum7
Zeros33786
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.584443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.341262999
Coefficient of variation (CV)2.003771883
Kurtosis0.7894047878
Mean1.168427913
Median Absolute Deviation (MAD)0
Skewness1.617544674
Sum50198
Variance5.481512433
MonotonicityNot monotonic
2021-12-29T15:59:32.667877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
033786
78.6%
65056
 
11.8%
71647
 
3.8%
31180
 
2.7%
5669
 
1.6%
2332
 
0.8%
4164
 
0.4%
1128
 
0.3%
ValueCountFrequency (%)
033786
78.6%
1128
 
0.3%
2332
 
0.8%
31180
 
2.7%
4164
 
0.4%
5669
 
1.6%
65056
 
11.8%
71647
 
3.8%
ValueCountFrequency (%)
71647
 
3.8%
65056
 
11.8%
5669
 
1.6%
4164
 
0.4%
31180
 
2.7%
2332
 
0.8%
1128
 
0.3%
033786
78.6%

D19_KONSUMTYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean3.695884448
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.760295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.761664716
Coefficient of variation (CV)0.7472270184
Kurtosis-0.2748055142
Mean3.695884448
Median Absolute Deviation (MAD)1
Skewness1.069897628
Sum130753
Variance7.626792005
MonotonicityNot monotonic
2021-12-29T15:59:32.831902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
311442
26.6%
17750
18.0%
96443
15.0%
25987
13.9%
41653
 
3.8%
61589
 
3.7%
5514
 
1.2%
(Missing)7584
17.7%
ValueCountFrequency (%)
17750
18.0%
25987
13.9%
311442
26.6%
41653
 
3.8%
5514
 
1.2%
61589
 
3.7%
96443
15.0%
ValueCountFrequency (%)
96443
15.0%
61589
 
3.7%
5514
 
1.2%
41653
 
3.8%
311442
26.6%
25987
13.9%
17750
18.0%

D19_KONSUMTYP_MAX
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.612518039
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:32.924103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q38
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.219637653
Coefficient of variation (CV)0.6980216934
Kurtosis-1.732328883
Mean4.612518039
Median Absolute Deviation (MAD)2
Skewness0.3138652574
Sum198163
Variance10.36606661
MonotonicityNot monotonic
2021-12-29T15:59:33.005640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
214297
33.3%
89673
22.5%
97584
17.7%
16366
14.8%
32605
 
6.1%
42437
 
5.7%
ValueCountFrequency (%)
16366
14.8%
214297
33.3%
32605
 
6.1%
42437
 
5.7%
89673
22.5%
97584
17.7%
ValueCountFrequency (%)
97584
17.7%
89673
22.5%
42437
 
5.7%
32605
 
6.1%
214297
33.3%
16366
14.8%

D19_KOSMETIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.108188632
Minimum0
Maximum7
Zeros28919
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:33.097486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.043675997
Coefficient of variation (CV)1.443739878
Kurtosis-1.359773033
Mean2.108188632
Median Absolute Deviation (MAD)0
Skewness0.7729636282
Sum90572
Variance9.263963577
MonotonicityNot monotonic
2021-12-29T15:59:33.178862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
028919
67.3%
67229
 
16.8%
76659
 
15.5%
374
 
0.2%
563
 
0.1%
48
 
< 0.1%
26
 
< 0.1%
14
 
< 0.1%
ValueCountFrequency (%)
028919
67.3%
14
 
< 0.1%
26
 
< 0.1%
374
 
0.2%
48
 
< 0.1%
563
 
0.1%
67229
 
16.8%
76659
 
15.5%
ValueCountFrequency (%)
76659
 
15.5%
67229
 
16.8%
563
 
0.1%
48
 
< 0.1%
374
 
0.2%
26
 
< 0.1%
14
 
< 0.1%
028919
67.3%

D19_LEBENSMITTEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6862343466
Minimum0
Maximum7
Zeros37467
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:33.268241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.853459469
Coefficient of variation (CV)2.700913293
Kurtosis4.266564666
Mean0.6862343466
Median Absolute Deviation (MAD)0
Skewness2.453815764
Sum29482
Variance3.435312004
MonotonicityNot monotonic
2021-12-29T15:59:33.342042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
037467
87.2%
63243
 
7.5%
3957
 
2.2%
5623
 
1.5%
7530
 
1.2%
281
 
0.2%
435
 
0.1%
126
 
0.1%
ValueCountFrequency (%)
037467
87.2%
126
 
0.1%
281
 
0.2%
3957
 
2.2%
435
 
0.1%
5623
 
1.5%
63243
 
7.5%
7530
 
1.2%
ValueCountFrequency (%)
7530
 
1.2%
63243
 
7.5%
5623
 
1.5%
435
 
0.1%
3957
 
2.2%
281
 
0.2%
126
 
0.1%
037467
87.2%

D19_LETZTER_KAUF_BRANCHE
Categorical

MISSING

Distinct35
Distinct (%)0.1%
Missing7584
Missing (%)17.7%
Memory size335.8 KiB
D19_UNBEKANNT
10276 
D19_SONSTIGE
2753 
D19_VERSICHERUNGEN
2662 
D19_VOLLSORTIMENT
2289 
D19_HAUS_DEKO
2224 
Other values (30)
15174 

Length

Max length22
Median length13
Mean length14.33017695
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD19_UNBEKANNT
2nd rowD19_TELKO_MOBILE
3rd rowD19_LEBENSMITTEL
4th rowD19_UNBEKANNT
5th rowD19_BEKLEIDUNG_GEH

Common Values

ValueCountFrequency (%)
D19_UNBEKANNT10276
23.9%
D19_SONSTIGE2753
 
6.4%
D19_VERSICHERUNGEN2662
 
6.2%
D19_VOLLSORTIMENT2289
 
5.3%
D19_HAUS_DEKO2224
 
5.2%
D19_BUCH_CD2089
 
4.9%
D19_DROGERIEARTIKEL1112
 
2.6%
D19_BEKLEIDUNG_REST1056
 
2.5%
D19_BEKLEIDUNG_GEH1054
 
2.5%
D19_SCHUHE1042
 
2.4%
Other values (25)8821
20.5%
(Missing)7584
17.7%

Length

2021-12-29T15:59:33.464409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d19_unbekannt10276
29.0%
d19_sonstige2753
 
7.8%
d19_versicherungen2662
 
7.5%
d19_vollsortiment2289
 
6.5%
d19_haus_deko2224
 
6.3%
d19_buch_cd2089
 
5.9%
d19_drogerieartikel1112
 
3.1%
d19_bekleidung_rest1056
 
3.0%
d19_bekleidung_geh1054
 
3.0%
d19_schuhe1042
 
2.9%
Other values (25)8821
24.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_LOTTO
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.905930239
Minimum0
Maximum7
Zeros20293
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:33.556172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.386266181
Coefficient of variation (CV)1.16529507
Kurtosis-1.877900534
Mean2.905930239
Median Absolute Deviation (MAD)0
Skewness0.320095582
Sum102806
Variance11.46679865
MonotonicityNot monotonic
2021-12-29T15:59:33.648291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
020293
47.2%
712714
29.6%
62174
 
5.1%
3108
 
0.3%
586
 
0.2%
42
 
< 0.1%
21
 
< 0.1%
(Missing)7584
 
17.7%
ValueCountFrequency (%)
020293
47.2%
21
 
< 0.1%
3108
 
0.3%
42
 
< 0.1%
586
 
0.2%
62174
 
5.1%
712714
29.6%
ValueCountFrequency (%)
712714
29.6%
62174
 
5.1%
586
 
0.2%
42
 
< 0.1%
3108
 
0.3%
21
 
< 0.1%
020293
47.2%

D19_NAHRUNGSERGAENZUNG
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.563497975
Minimum0
Maximum7
Zeros38508
Zeros (%)89.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:33.749552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.713365835
Coefficient of variation (CV)3.040589161
Kurtosis6.360326696
Mean0.563497975
Median Absolute Deviation (MAD)0
Skewness2.841325911
Sum24209
Variance2.935622484
MonotonicityNot monotonic
2021-12-29T15:59:33.829369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
038508
89.6%
62468
 
5.7%
3695
 
1.6%
7623
 
1.5%
5534
 
1.2%
255
 
0.1%
147
 
0.1%
432
 
0.1%
ValueCountFrequency (%)
038508
89.6%
147
 
0.1%
255
 
0.1%
3695
 
1.6%
432
 
0.1%
5534
 
1.2%
62468
 
5.7%
7623
 
1.5%
ValueCountFrequency (%)
7623
 
1.5%
62468
 
5.7%
5534
 
1.2%
432
 
0.1%
3695
 
1.6%
255
 
0.1%
147
 
0.1%
038508
89.6%

D19_RATGEBER
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9156463852
Minimum0
Maximum7
Zeros35306
Zeros (%)82.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:33.913230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.072453944
Coefficient of variation (CV)2.263378066
Kurtosis2.075685705
Mean0.9156463852
Median Absolute Deviation (MAD)0
Skewness1.964237218
Sum39338
Variance4.29506535
MonotonicityNot monotonic
2021-12-29T15:59:34.005196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035306
82.2%
64591
 
10.7%
31051
 
2.4%
2632
 
1.5%
5566
 
1.3%
7537
 
1.2%
4169
 
0.4%
1110
 
0.3%
ValueCountFrequency (%)
035306
82.2%
1110
 
0.3%
2632
 
1.5%
31051
 
2.4%
4169
 
0.4%
5566
 
1.3%
64591
 
10.7%
7537
 
1.2%
ValueCountFrequency (%)
7537
 
1.2%
64591
 
10.7%
5566
 
1.3%
4169
 
0.4%
31051
 
2.4%
2632
 
1.5%
1110
 
0.3%
035306
82.2%

D19_REISEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.060588427
Minimum0
Maximum7
Zeros28298
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.095119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.920401013
Coefficient of variation (CV)1.417265561
Kurtosis-1.357436218
Mean2.060588427
Median Absolute Deviation (MAD)0
Skewness0.7582206081
Sum88527
Variance8.528742076
MonotonicityNot monotonic
2021-12-29T15:59:34.168134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
028298
65.9%
69610
 
22.4%
73833
 
8.9%
3472
 
1.1%
5341
 
0.8%
2336
 
0.8%
457
 
0.1%
115
 
< 0.1%
ValueCountFrequency (%)
028298
65.9%
115
 
< 0.1%
2336
 
0.8%
3472
 
1.1%
457
 
0.1%
5341
 
0.8%
69610
 
22.4%
73833
 
8.9%
ValueCountFrequency (%)
73833
 
8.9%
69610
 
22.4%
5341
 
0.8%
457
 
0.1%
3472
 
1.1%
2336
 
0.8%
115
 
< 0.1%
028298
65.9%

D19_SAMMELARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.491899818
Minimum0
Maximum7
Zeros31986
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.250449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.579540198
Coefficient of variation (CV)1.729030439
Kurtosis-0.5389752606
Mean1.491899818
Median Absolute Deviation (MAD)0
Skewness1.185656488
Sum64095
Variance6.654027633
MonotonicityNot monotonic
2021-12-29T15:59:34.332160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
031986
74.5%
68904
 
20.7%
7814
 
1.9%
5593
 
1.4%
3532
 
1.2%
479
 
0.2%
242
 
0.1%
112
 
< 0.1%
ValueCountFrequency (%)
031986
74.5%
112
 
< 0.1%
242
 
0.1%
3532
 
1.2%
479
 
0.2%
5593
 
1.4%
68904
 
20.7%
7814
 
1.9%
ValueCountFrequency (%)
7814
 
1.9%
68904
 
20.7%
5593
 
1.4%
479
 
0.2%
3532
 
1.2%
242
 
0.1%
112
 
< 0.1%
031986
74.5%

D19_SCHUHE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5499278432
Minimum0
Maximum7
Zeros37508
Zeros (%)87.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.421557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.558936622
Coefficient of variation (CV)2.834802131
Kurtosis6.477371411
Mean0.5499278432
Median Absolute Deviation (MAD)0
Skewness2.79274952
Sum23626
Variance2.430283391
MonotonicityNot monotonic
2021-12-29T15:59:34.495523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
037508
87.3%
61827
 
4.3%
31715
 
4.0%
5863
 
2.0%
2529
 
1.2%
7214
 
0.5%
1192
 
0.4%
4114
 
0.3%
ValueCountFrequency (%)
037508
87.3%
1192
 
0.4%
2529
 
1.2%
31715
 
4.0%
4114
 
0.3%
5863
 
2.0%
61827
 
4.3%
7214
 
0.5%
ValueCountFrequency (%)
7214
 
0.5%
61827
 
4.3%
5863
 
2.0%
4114
 
0.3%
31715
 
4.0%
2529
 
1.2%
1192
 
0.4%
037508
87.3%

D19_SONSTIGE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.617941437
Minimum0
Maximum7
Zeros15258
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.587568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.892432674
Coefficient of variation (CV)0.7994691801
Kurtosis-1.723978843
Mean3.617941437
Median Absolute Deviation (MAD)2
Skewness-0.2962165328
Sum155434
Variance8.366166773
MonotonicityNot monotonic
2021-12-29T15:59:34.669545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
616183
37.7%
015258
35.5%
75162
 
12.0%
32891
 
6.7%
51969
 
4.6%
2712
 
1.7%
4491
 
1.1%
1296
 
0.7%
ValueCountFrequency (%)
015258
35.5%
1296
 
0.7%
2712
 
1.7%
32891
 
6.7%
4491
 
1.1%
51969
 
4.6%
616183
37.7%
75162
 
12.0%
ValueCountFrequency (%)
75162
 
12.0%
616183
37.7%
51969
 
4.6%
4491
 
1.1%
32891
 
6.7%
2712
 
1.7%
1296
 
0.7%
015258
35.5%

D19_SOZIALES
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean1.69410368
Minimum0
Maximum5
Zeros9615
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.769601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.510322811
Coefficient of variation (CV)0.8915173425
Kurtosis-0.9806251656
Mean1.69410368
Median Absolute Deviation (MAD)1
Skewness0.4988485443
Sum59934
Variance2.281074993
MonotonicityNot monotonic
2021-12-29T15:59:34.853552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
110991
25.6%
09615
22.4%
38624
20.1%
43196
 
7.4%
21491
 
3.5%
51461
 
3.4%
(Missing)7584
17.7%
ValueCountFrequency (%)
09615
22.4%
110991
25.6%
21491
 
3.5%
38624
20.1%
43196
 
7.4%
51461
 
3.4%
ValueCountFrequency (%)
51461
 
3.4%
43196
 
7.4%
38624
20.1%
21491
 
3.5%
110991
25.6%
09615
22.4%

D19_TECHNIK
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.460104278
Minimum0
Maximum7
Zeros25369
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:34.934878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.013667797
Coefficient of variation (CV)1.225016282
Kurtosis-1.710391779
Mean2.460104278
Median Absolute Deviation (MAD)0
Skewness0.4604807392
Sum105691
Variance9.082193588
MonotonicityNot monotonic
2021-12-29T15:59:35.014144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
025369
59.0%
611292
26.3%
74306
 
10.0%
5912
 
2.1%
3912
 
2.1%
486
 
0.2%
272
 
0.2%
113
 
< 0.1%
ValueCountFrequency (%)
025369
59.0%
113
 
< 0.1%
272
 
0.2%
3912
 
2.1%
486
 
0.2%
5912
 
2.1%
611292
26.3%
74306
 
10.0%
ValueCountFrequency (%)
74306
 
10.0%
611292
26.3%
5912
 
2.1%
486
 
0.2%
3912
 
2.1%
272
 
0.2%
113
 
< 0.1%
025369
59.0%

D19_TELKO_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05355895908
Minimum0
Maximum6
Zeros41099
Zeros (%)95.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:35.097663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2730818605
Coefficient of variation (CV)5.09871486
Kurtosis49.50906931
Mean0.05355895908
Median Absolute Deviation (MAD)0
Skewness6.231514103
Sum2301
Variance0.07457370255
MonotonicityNot monotonic
2021-12-29T15:59:35.189949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
041099
95.7%
11481
 
3.4%
2341
 
0.8%
329
 
0.1%
410
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
041099
95.7%
11481
 
3.4%
2341
 
0.8%
329
 
0.1%
410
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
51
 
< 0.1%
410
 
< 0.1%
329
 
0.1%
2341
 
0.8%
11481
 
3.4%
041099
95.7%

D19_TELKO_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09627112332
Minimum0
Maximum6
Zeros39720
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:35.271424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3688173323
Coefficient of variation (CV)3.831027618
Kurtosis26.3568111
Mean0.09627112332
Median Absolute Deviation (MAD)0
Skewness4.617613337
Sum4136
Variance0.1360262246
MonotonicityNot monotonic
2021-12-29T15:59:35.361032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
039720
92.5%
12469
 
5.7%
2684
 
1.6%
362
 
0.1%
423
 
0.1%
53
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
039720
92.5%
12469
 
5.7%
2684
 
1.6%
362
 
0.1%
423
 
0.1%
53
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
53
 
< 0.1%
423
 
0.1%
362
 
0.1%
2684
 
1.6%
12469
 
5.7%
039720
92.5%

D19_TELKO_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.465620781
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:35.454370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.33337358
Coefficient of variation (CV)0.1408648847
Kurtosis10.59028856
Mean9.465620781
Median Absolute Deviation (MAD)0
Skewness-3.157357748
Sum406662
Variance1.777885103
MonotonicityNot monotonic
2021-12-29T15:59:35.545992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1033571
78.1%
94563
 
10.6%
81586
 
3.7%
51351
 
3.1%
7706
 
1.6%
6673
 
1.6%
4224
 
0.5%
2100
 
0.2%
199
 
0.2%
389
 
0.2%
ValueCountFrequency (%)
199
 
0.2%
2100
 
0.2%
389
 
0.2%
4224
 
0.5%
51351
 
3.1%
6673
 
1.6%
7706
 
1.6%
81586
 
3.7%
94563
 
10.6%
1033571
78.1%
ValueCountFrequency (%)
1033571
78.1%
94563
 
10.6%
81586
 
3.7%
7706
 
1.6%
6673
 
1.6%
51351
 
3.1%
4224
 
0.5%
389
 
0.2%
2100
 
0.2%
199
 
0.2%

D19_TELKO_MOBILE
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8581769936
Minimum0
Maximum7
Zeros36119
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:35.627860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.034735216
Coefficient of variation (CV)2.370997162
Kurtosis2.355745115
Mean0.8581769936
Median Absolute Deviation (MAD)0
Skewness2.046708537
Sum36869
Variance4.140147399
MonotonicityNot monotonic
2021-12-29T15:59:35.892344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
036119
84.1%
64623
 
10.8%
3993
 
2.3%
5600
 
1.4%
7339
 
0.8%
4128
 
0.3%
2107
 
0.2%
153
 
0.1%
ValueCountFrequency (%)
036119
84.1%
153
 
0.1%
2107
 
0.2%
3993
 
2.3%
4128
 
0.3%
5600
 
1.4%
64623
 
10.8%
7339
 
0.8%
ValueCountFrequency (%)
7339
 
0.8%
64623
 
10.8%
5600
 
1.4%
4128
 
0.3%
3993
 
2.3%
2107
 
0.2%
153
 
0.1%
036119
84.1%

D19_TELKO_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.776127741
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:35.993677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9077051287
Coefficient of variation (CV)0.09284914771
Kurtosis27.54580377
Mean9.776127741
Median Absolute Deviation (MAD)0
Skewness-5.02947815
Sum420002
Variance0.8239286006
MonotonicityNot monotonic
2021-12-29T15:59:36.083112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1039183
91.2%
91734
 
4.0%
5828
 
1.9%
8707
 
1.6%
6230
 
0.5%
7149
 
0.3%
448
 
0.1%
232
 
0.1%
131
 
0.1%
320
 
< 0.1%
ValueCountFrequency (%)
131
 
0.1%
232
 
0.1%
320
 
< 0.1%
448
 
0.1%
5828
 
1.9%
6230
 
0.5%
7149
 
0.3%
8707
 
1.6%
91734
 
4.0%
1039183
91.2%
ValueCountFrequency (%)
1039183
91.2%
91734
 
4.0%
8707
 
1.6%
7149
 
0.3%
6230
 
0.5%
5828
 
1.9%
448
 
0.1%
320
 
< 0.1%
232
 
0.1%
131
 
0.1%

D19_TELKO_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.978841767
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:36.185261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2514253572
Coefficient of variation (CV)0.02519584568
Kurtosis370.0917397
Mean9.978841767
Median Absolute Deviation (MAD)0
Skewness-17.124395
Sum428711
Variance0.06321471023
MonotonicityNot monotonic
2021-12-29T15:59:36.267145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1042493
98.9%
9260
 
0.6%
8106
 
0.2%
740
 
0.1%
530
 
0.1%
623
 
0.1%
33
 
< 0.1%
23
 
< 0.1%
42
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
12
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
42
 
< 0.1%
530
 
0.1%
623
 
0.1%
740
 
0.1%
8106
 
0.2%
9260
 
0.6%
1042493
98.9%
ValueCountFrequency (%)
1042493
98.9%
9260
 
0.6%
8106
 
0.2%
740
 
0.1%
623
 
0.1%
530
 
0.1%
42
 
< 0.1%
33
 
< 0.1%
23
 
< 0.1%
12
 
< 0.1%

D19_TELKO_ONLINE_QUOTE_12
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Memory size335.8 KiB
0.0
35338 
10.0
 
37
5.0
 
3

Length

Max length4
Median length3
Mean length3.001045848
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035338
82.3%
10.037
 
0.1%
5.03
 
< 0.1%
(Missing)7584
 
17.7%

Length

2021-12-29T15:59:36.369334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:36.432762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035338
99.9%
10.037
 
0.1%
5.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_TELKO_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6160327731
Minimum0
Maximum7
Zeros37986
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:36.503923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.753162898
Coefficient of variation (CV)2.845892254
Kurtosis5.047523719
Mean0.6160327731
Median Absolute Deviation (MAD)0
Skewness2.609397689
Sum26466
Variance3.073580145
MonotonicityNot monotonic
2021-12-29T15:59:36.585893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
037986
88.4%
63061
 
7.1%
3804
 
1.9%
5723
 
1.7%
7232
 
0.5%
274
 
0.2%
473
 
0.2%
19
 
< 0.1%
ValueCountFrequency (%)
037986
88.4%
19
 
< 0.1%
274
 
0.2%
3804
 
1.9%
473
 
0.2%
5723
 
1.7%
63061
 
7.1%
7232
 
0.5%
ValueCountFrequency (%)
7232
 
0.5%
63061
 
7.1%
5723
 
1.7%
473
 
0.2%
3804
 
1.9%
274
 
0.2%
19
 
< 0.1%
037986
88.4%

D19_TIERARTIKEL
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2645826544
Minimum0
Maximum7
Zeros40917
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:36.677432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.223280465
Coefficient of variation (CV)4.623434094
Kurtosis19.83859882
Mean0.2645826544
Median Absolute Deviation (MAD)0
Skewness4.604508592
Sum11367
Variance1.496415097
MonotonicityNot monotonic
2021-12-29T15:59:36.758774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
040917
95.2%
6907
 
2.1%
7529
 
1.2%
3350
 
0.8%
5203
 
0.5%
232
 
0.1%
423
 
0.1%
11
 
< 0.1%
ValueCountFrequency (%)
040917
95.2%
11
 
< 0.1%
232
 
0.1%
3350
 
0.8%
423
 
0.1%
5203
 
0.5%
6907
 
2.1%
7529
 
1.2%
ValueCountFrequency (%)
7529
 
1.2%
6907
 
2.1%
5203
 
0.5%
423
 
0.1%
3350
 
0.8%
232
 
0.1%
11
 
< 0.1%
040917
95.2%

D19_VERSAND_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7746613286
Minimum0
Maximum6
Zeros27763
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:36.860893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.288737161
Coefficient of variation (CV)1.663613651
Kurtosis2.380534371
Mean0.7746613286
Median Absolute Deviation (MAD)0
Skewness1.7558783
Sum33281
Variance1.66084347
MonotonicityNot monotonic
2021-12-29T15:59:36.942144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
027763
64.6%
15826
 
13.6%
24545
 
10.6%
32033
 
4.7%
41877
 
4.4%
5750
 
1.7%
6168
 
0.4%
ValueCountFrequency (%)
027763
64.6%
15826
 
13.6%
24545
 
10.6%
32033
 
4.7%
41877
 
4.4%
5750
 
1.7%
6168
 
0.4%
ValueCountFrequency (%)
6168
 
0.4%
5750
 
1.7%
41877
 
4.4%
32033
 
4.7%
24545
 
10.6%
15826
 
13.6%
027763
64.6%

D19_VERSAND_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.193473302
Minimum0
Maximum6
Zeros23413
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.033925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.64398307
Coefficient of variation (CV)1.377477877
Kurtosis0.495740851
Mean1.193473302
Median Absolute Deviation (MAD)0
Skewness1.260563113
Sum51274
Variance2.702680336
MonotonicityNot monotonic
2021-12-29T15:59:37.114210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
023413
54.5%
25567
 
13.0%
15533
 
12.9%
43042
 
7.1%
32702
 
6.3%
51897
 
4.4%
6808
 
1.9%
ValueCountFrequency (%)
023413
54.5%
15533
 
12.9%
25567
 
13.0%
32702
 
6.3%
43042
 
7.1%
51897
 
4.4%
6808
 
1.9%
ValueCountFrequency (%)
6808
 
1.9%
51897
 
4.4%
43042
 
7.1%
32702
 
6.3%
25567
 
13.0%
15533
 
12.9%
023413
54.5%

D19_VERSAND_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.168590848
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.203846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.027906857
Coefficient of variation (CV)0.4223852248
Kurtosis-0.8576203016
Mean7.168590848
Median Absolute Deviation (MAD)1
Skewness-0.7258834537
Sum307977
Variance9.168219932
MonotonicityNot monotonic
2021-12-29T15:59:37.277789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1014836
34.5%
96948
16.2%
55055
 
11.8%
83064
 
7.1%
12851
 
6.6%
22485
 
5.8%
62240
 
5.2%
41960
 
4.6%
71907
 
4.4%
31616
 
3.8%
ValueCountFrequency (%)
12851
 
6.6%
22485
 
5.8%
31616
 
3.8%
41960
 
4.6%
55055
 
11.8%
62240
 
5.2%
71907
 
4.4%
83064
 
7.1%
96948
16.2%
1014836
34.5%
ValueCountFrequency (%)
1014836
34.5%
96948
16.2%
83064
 
7.1%
71907
 
4.4%
62240
 
5.2%
55055
 
11.8%
41960
 
4.6%
31616
 
3.8%
22485
 
5.8%
12851
 
6.6%

D19_VERSAND_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.471812299
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.380331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q18
median9
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.149832953
Coefficient of variation (CV)0.2537630529
Kurtosis1.9222907
Mean8.471812299
Median Absolute Deviation (MAD)1
Skewness-1.622498229
Sum363966
Variance4.621781725
MonotonicityNot monotonic
2021-12-29T15:59:37.461222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1019939
46.4%
99660
22.5%
83995
 
9.3%
53423
 
8.0%
71794
 
4.2%
61573
 
3.7%
2815
 
1.9%
4716
 
1.7%
1535
 
1.2%
3512
 
1.2%
ValueCountFrequency (%)
1535
 
1.2%
2815
 
1.9%
3512
 
1.2%
4716
 
1.7%
53423
 
8.0%
61573
 
3.7%
71794
 
4.2%
83995
 
9.3%
99660
22.5%
1019939
46.4%
ValueCountFrequency (%)
1019939
46.4%
99660
22.5%
83995
 
9.3%
71794
 
4.2%
61573
 
3.7%
53423
 
8.0%
4716
 
1.7%
3512
 
1.2%
2815
 
1.9%
1535
 
1.2%

D19_VERSAND_ONLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.82775476
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.560132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.937353179
Coefficient of variation (CV)0.3752484932
Kurtosis-0.1892625845
Mean7.82775476
Median Absolute Deviation (MAD)0
Skewness-1.105158743
Sum336296
Variance8.628043696
MonotonicityNot monotonic
2021-12-29T15:59:37.633327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1021845
50.8%
95045
 
11.7%
53582
 
8.3%
12335
 
5.4%
82097
 
4.9%
61861
 
4.3%
21846
 
4.3%
41585
 
3.7%
71481
 
3.4%
31285
 
3.0%
ValueCountFrequency (%)
12335
 
5.4%
21846
 
4.3%
31285
 
3.0%
41585
 
3.7%
53582
 
8.3%
61861
 
4.3%
71481
 
3.4%
82097
 
4.9%
95045
 
11.7%
1021845
50.8%
ValueCountFrequency (%)
1021845
50.8%
95045
 
11.7%
82097
 
4.9%
71481
 
3.4%
61861
 
4.3%
53582
 
8.3%
41585
 
3.7%
31285
 
3.0%
21846
 
4.3%
12335
 
5.4%

D19_VERSAND_ONLINE_QUOTE_12
Real number (ℝ≥0)

MISSING
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.61620216
Minimum0
Maximum10
Zeros24597
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.724559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.183047724
Coefficient of variation (CV)1.598900799
Kurtosis-0.7358216122
Mean2.61620216
Median Absolute Deviation (MAD)0
Skewness1.074200242
Sum92556
Variance17.49788826
MonotonicityNot monotonic
2021-12-29T15:59:37.815909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
024597
57.3%
107494
 
17.4%
5916
 
2.1%
8496
 
1.2%
3486
 
1.1%
7439
 
1.0%
9280
 
0.7%
1203
 
0.5%
2178
 
0.4%
6151
 
0.4%
(Missing)7584
 
17.7%
ValueCountFrequency (%)
024597
57.3%
1203
 
0.5%
2178
 
0.4%
3486
 
1.1%
4138
 
0.3%
5916
 
2.1%
6151
 
0.4%
7439
 
1.0%
8496
 
1.2%
9280
 
0.7%
ValueCountFrequency (%)
107494
17.4%
9280
 
0.7%
8496
 
1.2%
7439
 
1.0%
6151
 
0.4%
5916
 
2.1%
4138
 
0.3%
3486
 
1.1%
2178
 
0.4%
1203
 
0.5%

D19_VERSAND_REST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7223592943
Minimum0
Maximum7
Zeros36458
Zeros (%)84.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:37.897412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.807373135
Coefficient of variation (CV)2.502041781
Kurtosis3.65514105
Mean0.7223592943
Median Absolute Deviation (MAD)0
Skewness2.298122007
Sum31034
Variance3.26659765
MonotonicityNot monotonic
2021-12-29T15:59:37.999390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
036458
84.9%
63009
 
7.0%
31778
 
4.1%
51022
 
2.4%
2239
 
0.6%
7192
 
0.4%
4150
 
0.3%
1114
 
0.3%
ValueCountFrequency (%)
036458
84.9%
1114
 
0.3%
2239
 
0.6%
31778
 
4.1%
4150
 
0.3%
51022
 
2.4%
63009
 
7.0%
7192
 
0.4%
ValueCountFrequency (%)
7192
 
0.4%
63009
 
7.0%
51022
 
2.4%
4150
 
0.3%
31778
 
4.1%
2239
 
0.6%
1114
 
0.3%
036458
84.9%

D19_VERSI_ANZ_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1132163307
Minimum0
Maximum6
Zeros39485
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:38.090655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.422899479
Coefficient of variation (CV)3.735322249
Kurtosis23.59097344
Mean0.1132163307
Median Absolute Deviation (MAD)0
Skewness4.467679767
Sum4864
Variance0.1788439694
MonotonicityNot monotonic
2021-12-29T15:59:38.170211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
039485
91.9%
12327
 
5.4%
2965
 
2.2%
3141
 
0.3%
437
 
0.1%
56
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
039485
91.9%
12327
 
5.4%
2965
 
2.2%
3141
 
0.3%
437
 
0.1%
56
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
56
 
< 0.1%
437
 
0.1%
3141
 
0.3%
2965
 
2.2%
12327
 
5.4%
039485
91.9%

D19_VERSI_ANZ_24
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1961966389
Minimum0
Maximum6
Zeros37473
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:38.254375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58282604
Coefficient of variation (CV)2.97062194
Kurtosis14.58017382
Mean0.1961966389
Median Absolute Deviation (MAD)0
Skewness3.567704655
Sum8429
Variance0.3396861928
MonotonicityNot monotonic
2021-12-29T15:59:38.326001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
037473
87.2%
13287
 
7.7%
21668
 
3.9%
3355
 
0.8%
4156
 
0.4%
521
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
037473
87.2%
13287
 
7.7%
21668
 
3.9%
3355
 
0.8%
4156
 
0.4%
521
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
521
 
< 0.1%
4156
 
0.4%
3355
 
0.8%
21668
 
3.9%
13287
 
7.7%
037473
87.2%

D19_VERSI_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.156440575
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:38.418296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.906264354
Coefficient of variation (CV)0.2081883607
Kurtosis6.062519874
Mean9.156440575
Median Absolute Deviation (MAD)0
Skewness-2.575567305
Sum393379
Variance3.633843786
MonotonicityNot monotonic
2021-12-29T15:59:38.508385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1032256
75.1%
93611
 
8.4%
51637
 
3.8%
81606
 
3.7%
61110
 
2.6%
7902
 
2.1%
2750
 
1.7%
1416
 
1.0%
4375
 
0.9%
3299
 
0.7%
ValueCountFrequency (%)
1416
 
1.0%
2750
 
1.7%
3299
 
0.7%
4375
 
0.9%
51637
 
3.8%
61110
 
2.6%
7902
 
2.1%
81606
 
3.7%
93611
 
8.4%
1032256
75.1%
ValueCountFrequency (%)
1032256
75.1%
93611
 
8.4%
81606
 
3.7%
7902
 
2.1%
61110
 
2.6%
51637
 
3.8%
4375
 
0.9%
3299
 
0.7%
2750
 
1.7%
1416
 
1.0%

D19_VERSI_OFFLINE_DATUM
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.898887389
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:38.591844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6328669929
Coefficient of variation (CV)0.06393314401
Kurtosis55.71106942
Mean9.898887389
Median Absolute Deviation (MAD)0
Skewness-7.263246089
Sum425276
Variance0.4005206307
MonotonicityNot monotonic
2021-12-29T15:59:38.683435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1041452
96.5%
9563
 
1.3%
5485
 
1.1%
8272
 
0.6%
6106
 
0.2%
751
 
0.1%
413
 
< 0.1%
39
 
< 0.1%
16
 
< 0.1%
25
 
< 0.1%
ValueCountFrequency (%)
16
 
< 0.1%
25
 
< 0.1%
39
 
< 0.1%
413
 
< 0.1%
5485
 
1.1%
6106
 
0.2%
751
 
0.1%
8272
 
0.6%
9563
 
1.3%
1041452
96.5%
ValueCountFrequency (%)
1041452
96.5%
9563
 
1.3%
8272
 
0.6%
751
 
0.1%
6106
 
0.2%
5485
 
1.1%
413
 
< 0.1%
39
 
< 0.1%
25
 
< 0.1%
16
 
< 0.1%

D19_VERSI_ONLINE_DATUM
Real number (ℝ≥0)

SKEWED

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.984288441
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:38.794640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2582136755
Coefficient of variation (CV)0.02586200079
Kurtosis462.2149483
Mean9.984288441
Median Absolute Deviation (MAD)0
Skewness-20.21632487
Sum428945
Variance0.06667430222
MonotonicityNot monotonic
2021-12-29T15:59:38.885462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1042735
99.5%
973
 
0.2%
843
 
0.1%
538
 
0.1%
730
 
0.1%
621
 
< 0.1%
411
 
< 0.1%
25
 
< 0.1%
34
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
12
 
< 0.1%
25
 
< 0.1%
34
 
< 0.1%
411
 
< 0.1%
538
 
0.1%
621
 
< 0.1%
730
 
0.1%
843
 
0.1%
973
 
0.2%
1042735
99.5%
ValueCountFrequency (%)
1042735
99.5%
973
 
0.2%
843
 
0.1%
730
 
0.1%
621
 
< 0.1%
538
 
0.1%
411
 
< 0.1%
34
 
< 0.1%
25
 
< 0.1%
12
 
< 0.1%

D19_VERSI_ONLINE_QUOTE_12
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7584
Missing (%)17.7%
Memory size335.8 KiB
0.0
35318 
10.0
 
54
5.0
 
5
3.0
 
1

Length

Max length4
Median length3
Mean length3.001526372
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035318
82.2%
10.054
 
0.1%
5.05
 
< 0.1%
3.01
 
< 0.1%
(Missing)7584
 
17.7%

Length

2021-12-29T15:59:38.996382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:39.067277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035318
99.8%
10.054
 
0.2%
5.05
 
< 0.1%
3.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

D19_VERSICHERUNGEN
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.195381965
Minimum0
Maximum7
Zeros32015
Zeros (%)74.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:39.146655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.199961507
Coefficient of variation (CV)1.840383719
Kurtosis0.464544979
Mean1.195381965
Median Absolute Deviation (MAD)0
Skewness1.482638204
Sum51356
Variance4.839830632
MonotonicityNot monotonic
2021-12-29T15:59:39.230506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
032015
74.5%
65217
 
12.1%
32327
 
5.4%
51474
 
3.4%
2714
 
1.7%
4538
 
1.3%
1436
 
1.0%
7241
 
0.6%
ValueCountFrequency (%)
032015
74.5%
1436
 
1.0%
2714
 
1.7%
32327
 
5.4%
4538
 
1.3%
51474
 
3.4%
65217
 
12.1%
7241
 
0.6%
ValueCountFrequency (%)
7241
 
0.6%
65217
 
12.1%
51474
 
3.4%
4538
 
1.3%
32327
 
5.4%
2714
 
1.7%
1436
 
1.0%
032015
74.5%

D19_VOLLSORTIMENT
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.845374983
Minimum0
Maximum7
Zeros20628
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:39.322796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.892541053
Coefficient of variation (CV)1.016576399
Kurtosis-1.831033011
Mean2.845374983
Median Absolute Deviation (MAD)3
Skewness0.1539149855
Sum122243
Variance8.366793743
MonotonicityNot monotonic
2021-12-29T15:59:39.404081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
020628
48.0%
614261
33.2%
33100
 
7.2%
72388
 
5.6%
51721
 
4.0%
2442
 
1.0%
4250
 
0.6%
1172
 
0.4%
ValueCountFrequency (%)
020628
48.0%
1172
 
0.4%
2442
 
1.0%
33100
 
7.2%
4250
 
0.6%
51721
 
4.0%
614261
33.2%
72388
 
5.6%
ValueCountFrequency (%)
72388
 
5.6%
614261
33.2%
51721
 
4.0%
4250
 
0.6%
33100
 
7.2%
2442
 
1.0%
1172
 
0.4%
020628
48.0%

D19_WEIN_FEINKOST
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8420930124
Minimum0
Maximum7
Zeros36950
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:39.505429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.123263069
Coefficient of variation (CV)2.521411575
Kurtosis2.922960589
Mean0.8420930124
Median Absolute Deviation (MAD)0
Skewness2.188275769
Sum36178
Variance4.50824606
MonotonicityNot monotonic
2021-12-29T15:59:39.586215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
036950
86.0%
63238
 
7.5%
71894
 
4.4%
5414
 
1.0%
3387
 
0.9%
452
 
0.1%
226
 
0.1%
11
 
< 0.1%
ValueCountFrequency (%)
036950
86.0%
11
 
< 0.1%
226
 
0.1%
3387
 
0.9%
452
 
0.1%
5414
 
1.0%
63238
 
7.5%
71894
 
4.4%
ValueCountFrequency (%)
71894
 
4.4%
63238
 
7.5%
5414
 
1.0%
452
 
0.1%
3387
 
0.9%
226
 
0.1%
11
 
< 0.1%
036950
86.0%

DSL_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
1.0
34500 
0.0
 
685

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.034500
80.3%
0.0685
 
1.6%
(Missing)7777
 
18.1%

Length

2021-12-29T15:59:39.688044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:39.749157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.034500
98.1%
0.0685
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEFUEGT_AM
Categorical

HIGH CARDINALITY
MISSING

Distinct1599
Distinct (%)4.5%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
1992-02-10 00:00:00
18156 
1992-02-12 00:00:00
8167 
1995-02-07 00:00:00
 
519
1993-03-01 00:00:00
 
249
2003-11-18 00:00:00
 
215
Other values (1594)
7879 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique683 ?
Unique (%)1.9%

Sample

1st row1992-02-10 00:00:00
2nd row1997-05-14 00:00:00
3rd row1995-05-24 00:00:00
4th row1992-02-10 00:00:00
5th row1992-02-10 00:00:00

Common Values

ValueCountFrequency (%)
1992-02-10 00:00:0018156
42.3%
1992-02-12 00:00:008167
19.0%
1995-02-07 00:00:00519
 
1.2%
1993-03-01 00:00:00249
 
0.6%
2003-11-18 00:00:00215
 
0.5%
2005-12-16 00:00:00149
 
0.3%
1995-10-17 00:00:00111
 
0.3%
1993-09-21 00:00:0098
 
0.2%
1994-02-03 00:00:0093
 
0.2%
2004-04-14 00:00:0092
 
0.2%
Other values (1589)7336
17.1%
(Missing)7777
18.1%

Length

2021-12-29T15:59:39.810016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:0035185
50.0%
1992-02-1018156
25.8%
1992-02-128167
 
11.6%
1995-02-07519
 
0.7%
1993-03-01249
 
0.4%
2003-11-18215
 
0.3%
2005-12-16149
 
0.2%
1995-10-17111
 
0.2%
1993-09-2198
 
0.1%
1994-02-0393
 
0.1%
Other values (1590)7428
 
10.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EINGEZOGENAM_HH_JAHR
Real number (ℝ≥0)

MISSING

Distinct33
Distinct (%)0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean1998.87367
Minimum1986
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:39.912074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1986
5-th percentile1994
Q11994
median1997
Q32002
95-th percentile2011
Maximum2018
Range32
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.776401974
Coefficient of variation (CV)0.002889828437
Kurtosis0.2697256702
Mean1998.87367
Median Absolute Deviation (MAD)3
Skewness1.102881098
Sum71945460
Variance33.36681976
MonotonicityNot monotonic
2021-12-29T15:59:40.023536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
199414397
33.5%
19975154
 
12.0%
20042011
 
4.7%
20011349
 
3.1%
19991348
 
3.1%
19981325
 
3.1%
20001287
 
3.0%
20021203
 
2.8%
20071102
 
2.6%
2008830
 
1.9%
Other values (23)5987
13.9%
(Missing)6969
16.2%
ValueCountFrequency (%)
19861
 
< 0.1%
19872
 
< 0.1%
19883
 
< 0.1%
198916
 
< 0.1%
199022
 
0.1%
199133
 
0.1%
199245
 
0.1%
199377
 
0.2%
199414397
33.5%
1995395
 
0.9%
ValueCountFrequency (%)
201827
 
0.1%
20176
 
< 0.1%
201618
 
< 0.1%
2015488
1.1%
2014412
1.0%
2013381
0.9%
2012394
0.9%
2011426
1.0%
2010389
0.9%
2009470
1.1%

EWDICHTE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7799
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean3.78696357
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:40.125609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.735390185
Coefficient of variation (CV)0.458253731
Kurtosis-1.331905684
Mean3.78696357
Median Absolute Deviation (MAD)2
Skewness-0.1889670668
Sum133161
Variance3.011579094
MonotonicityNot monotonic
2021-12-29T15:59:40.207019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
67918
18.4%
26721
15.6%
56720
15.6%
45735
13.3%
14268
9.9%
33801
8.8%
(Missing)7799
18.2%
ValueCountFrequency (%)
14268
9.9%
26721
15.6%
33801
8.8%
45735
13.3%
56720
15.6%
67918
18.4%
ValueCountFrequency (%)
67918
18.4%
56720
15.6%
45735
13.3%
33801
8.8%
26721
15.6%
14268
9.9%

EXTSEL992
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)0.2%
Missing15948
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean42.68434886
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:40.340360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q134
median47
Q355
95-th percentile56
Maximum56
Range55
Interquartile range (IQR)21

Descriptive statistics

Standard deviation13.56158044
Coefficient of variation (CV)0.3177178709
Kurtosis-0.02078671672
Mean42.68434886
Median Absolute Deviation (MAD)9
Skewness-0.8364997453
Sum1153075
Variance183.916464
MonotonicityNot monotonic
2021-12-29T15:59:40.451837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
566195
 
14.4%
552507
 
5.8%
361407
 
3.3%
311281
 
3.0%
541232
 
2.9%
351227
 
2.9%
501177
 
2.7%
531150
 
2.7%
34940
 
2.2%
23731
 
1.7%
Other values (46)9167
21.3%
(Missing)15948
37.1%
ValueCountFrequency (%)
159
 
0.1%
2138
0.3%
3138
0.3%
447
 
0.1%
551
 
0.1%
6207
0.5%
714
 
< 0.1%
815
 
< 0.1%
957
 
0.1%
1047
 
0.1%
ValueCountFrequency (%)
566195
14.4%
552507
5.8%
541232
 
2.9%
531150
 
2.7%
52328
 
0.8%
5196
 
0.2%
501177
 
2.7%
4923
 
0.1%
48554
 
1.3%
47341
 
0.8%

FINANZ_ANLEGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
1
18612 
2
9065 
5
8326 
3
4301 
4
2658 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
118612
43.3%
29065
21.1%
58326
19.4%
34301
 
10.0%
42658
 
6.2%

Length

2021-12-29T15:59:40.554135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:40.625587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
118612
43.3%
29065
21.1%
58326
19.4%
34301
 
10.0%
42658
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_HAUSBAUER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
3
14074 
5
10348 
2
8361 
4
5853 
1
4326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row3
3rd row2
4th row5
5th row5

Common Values

ValueCountFrequency (%)
314074
32.8%
510348
24.1%
28361
19.5%
45853
13.6%
14326
 
10.1%

Length

2021-12-29T15:59:40.717046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:40.787735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
314074
32.8%
510348
24.1%
28361
19.5%
45853
13.6%
14326
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_MINIMALIST
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
5
14764 
3
14297 
4
8451 
2
4842 
1
 
608

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row5
4th row4
5th row3

Common Values

ValueCountFrequency (%)
514764
34.4%
314297
33.3%
48451
19.7%
24842
 
11.3%
1608
 
1.4%

Length

2021-12-29T15:59:40.869513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:40.930246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
514764
34.4%
314297
33.3%
48451
19.7%
24842
 
11.3%
1608
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_SPARER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
1
26360 
4
8046 
2
5580 
3
 
2359
5
 
617

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
126360
61.4%
48046
 
18.7%
25580
 
13.0%
32359
 
5.5%
5617
 
1.4%

Length

2021-12-29T15:59:41.019673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:41.082661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
126360
61.4%
48046
 
18.7%
25580
 
13.0%
32359
 
5.5%
5617
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_UNAUFFAELLIGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
1
19177 
2
10483 
5
7957 
3
4325 
4
 
1020

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
119177
44.6%
210483
24.4%
57957
18.5%
34325
 
10.1%
41020
 
2.4%

Length

2021-12-29T15:59:41.164240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:41.235380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
119177
44.6%
210483
24.4%
57957
18.5%
34325
 
10.1%
41020
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZ_VORSORGER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
5
24524 
3
9215 
4
7557 
2
 
837
1
 
829

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
524524
57.1%
39215
 
21.4%
47557
 
17.6%
2837
 
1.9%
1829
 
1.9%

Length

2021-12-29T15:59:41.316705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:41.632302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
524524
57.1%
39215
 
21.4%
47557
 
17.6%
2837
 
1.9%
1829
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FINANZTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.27047158
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:41.703275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.668446465
Coefficient of variation (CV)0.3906937288
Kurtosis-1.304985555
Mean4.27047158
Median Absolute Deviation (MAD)1
Skewness-0.4494843445
Sum183468
Variance2.783713606
MonotonicityNot monotonic
2021-12-29T15:59:41.784444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
615058
35.0%
210791
25.1%
57585
17.7%
47447
17.3%
11209
 
2.8%
3872
 
2.0%
ValueCountFrequency (%)
11209
 
2.8%
210791
25.1%
3872
 
2.0%
47447
17.3%
57585
17.7%
615058
35.0%
ValueCountFrequency (%)
615058
35.0%
57585
17.7%
47447
17.3%
3872
 
2.0%
210791
25.1%
11209
 
2.8%

FIRMENDICHTE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
4.0
13005 
3.0
8254 
5.0
7350 
2.0
5232 
1.0
1344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.013005
30.3%
3.08254
19.2%
5.07350
17.1%
2.05232
12.2%
1.01344
 
3.1%
(Missing)7777
18.1%

Length

2021-12-29T15:59:41.895961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:41.966784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.013005
37.0%
3.08254
23.5%
5.07350
20.9%
2.05232
14.9%
1.01344
 
3.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBAEUDETYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.52860594
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:42.047591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.517929443
Coefficient of variation (CV)0.9957777144
Kurtosis0.6654017961
Mean2.52860594
Median Absolute Deviation (MAD)0
Skewness1.507798841
Sum88969
Variance6.339968682
MonotonicityNot monotonic
2021-12-29T15:59:42.118638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
122260
51.8%
36753
 
15.7%
85642
 
13.1%
2436
 
1.0%
461
 
0.1%
633
 
0.1%
(Missing)7777
 
18.1%
ValueCountFrequency (%)
122260
51.8%
2436
 
1.0%
36753
 
15.7%
461
 
0.1%
633
 
0.1%
85642
 
13.1%
ValueCountFrequency (%)
85642
 
13.1%
633
 
0.1%
461
 
0.1%
36753
 
15.7%
2436
 
1.0%
122260
51.8%

GEBAEUDETYP_RASTER
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
4.0
16815 
3.0
8436 
5.0
7350 
2.0
2069 
1.0
 
515

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.016815
39.1%
3.08436
19.6%
5.07350
17.1%
2.02069
 
4.8%
1.0515
 
1.2%
(Missing)7777
18.1%

Length

2021-12-29T15:59:42.230272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:42.299518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.016815
47.8%
3.08436
24.0%
5.07350
20.9%
2.02069
 
5.9%
1.0515
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GEBURTSJAHR
Real number (ℝ≥0)

ZEROS

Distinct108
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1155.7056
Minimum0
Maximum2017
Zeros17475
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:42.403772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1934
Q31949
95-th percentile1970
Maximum2017
Range2017
Interquartile range (IQR)1949

Descriptive statistics

Standard deviation957.0475869
Coefficient of variation (CV)0.8281067312
Kurtosis-1.855674476
Mean1155.7056
Median Absolute Deviation (MAD)29
Skewness-0.3792022153
Sum49651424
Variance915940.0837
MonotonicityNot monotonic
2021-12-29T15:59:42.526231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017475
40.7%
1941918
 
2.1%
1939871
 
2.0%
1940847
 
2.0%
1938791
 
1.8%
1936731
 
1.7%
1935710
 
1.7%
1943694
 
1.6%
1942692
 
1.6%
1937682
 
1.6%
Other values (98)18551
43.2%
ValueCountFrequency (%)
017475
40.7%
19051
 
< 0.1%
19081
 
< 0.1%
19092
 
< 0.1%
19101
 
< 0.1%
19113
 
< 0.1%
19121
 
< 0.1%
19137
 
< 0.1%
19143
 
< 0.1%
19157
 
< 0.1%
ValueCountFrequency (%)
20174
 
< 0.1%
20164
 
< 0.1%
201510
< 0.1%
20144
 
< 0.1%
20136
< 0.1%
20129
< 0.1%
20111
 
< 0.1%
20101
 
< 0.1%
20092
 
< 0.1%
20081
 
< 0.1%

GEMEINDETYP
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing7955
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean25.25286371
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:42.648770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median22
Q340
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)28

Descriptive statistics

Standard deviation12.54814148
Coefficient of variation (CV)0.4968997428
Kurtosis-0.9216244006
Mean25.25286371
Median Absolute Deviation (MAD)10
Skewness0.4845956535
Sum884027
Variance157.4558547
MonotonicityNot monotonic
2021-12-29T15:59:42.730787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
116987
16.3%
226466
15.1%
406020
14.0%
305761
13.4%
124282
10.0%
502917
 
6.8%
212574
 
6.0%
(Missing)7955
18.5%
ValueCountFrequency (%)
116987
16.3%
124282
10.0%
212574
 
6.0%
226466
15.1%
305761
13.4%
406020
14.0%
502917
6.8%
ValueCountFrequency (%)
502917
6.8%
406020
14.0%
305761
13.4%
226466
15.1%
212574
 
6.0%
124282
10.0%
116987
16.3%

GFK_URLAUBERTYP
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean6.514531246
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:42.832492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.017390282
Coefficient of variation (CV)0.4631784188
Kurtosis-0.9985352663
Mean6.514531246
Median Absolute Deviation (MAD)2
Skewness0.1679486803
Sum275936
Variance9.104644112
MonotonicityNot monotonic
2021-12-29T15:59:42.913955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
59812
22.8%
106070
14.1%
84301
10.0%
44281
10.0%
33803
 
8.9%
73420
 
8.0%
122450
 
5.7%
112045
 
4.8%
11838
 
4.3%
61653
 
3.8%
Other values (2)2684
 
6.2%
ValueCountFrequency (%)
11838
 
4.3%
21216
 
2.8%
33803
 
8.9%
44281
10.0%
59812
22.8%
61653
 
3.8%
73420
 
8.0%
84301
10.0%
91468
 
3.4%
106070
14.1%
ValueCountFrequency (%)
122450
 
5.7%
112045
 
4.8%
106070
14.1%
91468
 
3.4%
84301
10.0%
73420
 
8.0%
61653
 
3.8%
59812
22.8%
44281
10.0%
33803
 
8.9%

GREEN_AVANTGARDE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
0
30939 
1
12023 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
030939
72.0%
112023
 
28.0%

Length

2021-12-29T15:59:43.003626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:43.056606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
030939
72.0%
112023
 
28.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HEALTH_TYP
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
2
17053 
1
10519 
3
7993 
-1
7397 

Length

Max length2
Median length1
Mean length1.172175411
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row3

Common Values

ValueCountFrequency (%)
217053
39.7%
110519
24.5%
37993
18.6%
-17397
17.2%

Length

2021-12-29T15:59:43.127538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:43.198901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
117916
41.7%
217053
39.7%
37993
18.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_DELTA_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing9678
Missing (%)22.5%
Memory size335.8 KiB
0.0
28993 
1.0
4291 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.028993
67.5%
1.04291
 
10.0%
(Missing)9678
 
22.5%

Length

2021-12-29T15:59:43.280620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:43.350046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.028993
87.1%
1.04291
 
12.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HH_EINKOMMEN_SCORE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing704
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean3.559113067
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:43.401101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.657999028
Coefficient of variation (CV)0.4658461242
Kurtosis-1.332669251
Mean3.559113067
Median Absolute Deviation (MAD)2
Skewness0.04993306401
Sum150401
Variance2.748960777
MonotonicityNot monotonic
2021-12-29T15:59:43.475044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
211273
26.2%
57835
18.2%
66958
16.2%
46950
16.2%
34945
11.5%
14297
 
10.0%
(Missing)704
 
1.6%
ValueCountFrequency (%)
14297
 
10.0%
211273
26.2%
34945
11.5%
46950
16.2%
57835
18.2%
66958
16.2%
ValueCountFrequency (%)
66958
16.2%
57835
18.2%
46950
16.2%
34945
11.5%
211273
26.2%
14297
 
10.0%

INNENSTADT
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing7799
Missing (%)18.2%
Infinite0
Infinite (%)0.0%
Mean4.739129198
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:43.566502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.01026907
Coefficient of variation (CV)0.4241853273
Kurtosis-0.918889531
Mean4.739129198
Median Absolute Deviation (MAD)1
Skewness-0.02683364011
Sum166642
Variance4.041181734
MonotonicityNot monotonic
2021-12-29T15:59:43.646412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
56475
15.1%
45950
13.8%
65213
12.1%
24234
9.9%
84192
9.8%
33990
9.3%
73351
7.8%
11758
 
4.1%
(Missing)7799
18.2%
ValueCountFrequency (%)
11758
 
4.1%
24234
9.9%
33990
9.3%
45950
13.8%
56475
15.1%
65213
12.1%
73351
7.8%
84192
9.8%
ValueCountFrequency (%)
84192
9.8%
73351
7.8%
65213
12.1%
56475
15.1%
45950
13.8%
33990
9.3%
24234
9.9%
11758
 
4.1%

KBA05_ALTER1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.801859299
Minimum0
Maximum9
Zeros5246
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:43.729968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.340189404
Coefficient of variation (CV)0.7437813841
Kurtosis7.342953579
Mean1.801859299
Median Absolute Deviation (MAD)1
Skewness1.678734062
Sum61829
Variance1.796107638
MonotonicityNot monotonic
2021-12-29T15:59:43.811255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
211126
25.9%
19218
21.5%
36452
15.0%
05246
12.2%
41889
 
4.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
05246
12.2%
19218
21.5%
211126
25.9%
36452
15.0%
41889
 
4.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
41889
 
4.4%
36452
15.0%
211126
25.9%
19218
21.5%
05246
12.2%

KBA05_ALTER2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.901410503
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:43.892395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.199741952
Coefficient of variation (CV)0.4135030017
Kurtosis5.794001462
Mean2.901410503
Median Absolute Deviation (MAD)1
Skewness1.406381264
Sum99559
Variance1.439380752
MonotonicityNot monotonic
2021-12-29T15:59:43.983580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313695
31.9%
28949
20.8%
46054
14.1%
13313
 
7.7%
51920
 
4.5%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
13313
 
7.7%
28949
20.8%
313695
31.9%
46054
14.1%
51920
 
4.5%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
51920
 
4.5%
46054
14.1%
313695
31.9%
28949
20.8%
13313
 
7.7%

KBA05_ALTER3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.138252608
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:44.075497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.204453234
Coefficient of variation (CV)0.3837974135
Kurtosis4.696540955
Mean3.138252608
Median Absolute Deviation (MAD)1
Skewness1.147877142
Sum107686
Variance1.450707593
MonotonicityNot monotonic
2021-12-29T15:59:44.156628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313905
32.4%
47736
18.0%
26750
15.7%
53135
 
7.3%
12405
 
5.6%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12405
 
5.6%
26750
15.7%
313905
32.4%
47736
18.0%
53135
 
7.3%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
53135
 
7.3%
47736
18.0%
313905
32.4%
26750
15.7%
12405
 
5.6%

KBA05_ALTER4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.220726234
Minimum0
Maximum9
Zeros830
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:44.248238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.260912146
Coefficient of variation (CV)0.3914993249
Kurtosis4.004983825
Mean3.220726234
Median Absolute Deviation (MAD)1
Skewness0.6557965059
Sum110516
Variance1.589899441
MonotonicityNot monotonic
2021-12-29T15:59:44.329606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
314330
33.4%
48316
19.4%
25056
 
11.8%
53826
 
8.9%
11573
 
3.7%
0830
 
1.9%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
0830
 
1.9%
11573
 
3.7%
25056
 
11.8%
314330
33.4%
48316
19.4%
53826
 
8.9%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
53826
 
8.9%
48316
19.4%
314330
33.4%
25056
 
11.8%
11573
 
3.7%
0830
 
1.9%

KBA05_ANHANG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
15248 
0.0
9912 
3.0
4812 
2.0
3981 
9.0
 
361

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.015248
35.5%
0.09912
23.1%
3.04812
 
11.2%
2.03981
 
9.3%
9.0361
 
0.8%
(Missing)8648
20.1%

Length

2021-12-29T15:59:44.430797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:44.501702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.015248
44.4%
0.09912
28.9%
3.04812
 
14.0%
2.03981
 
11.6%
9.0361
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
9114 
3.0
6924 
2.0
6591 
4.0
5931 
1.0
5754 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.09114
21.2%
3.06924
16.1%
2.06591
15.3%
4.05931
13.8%
1.05754
13.4%
(Missing)8648
20.1%

Length

2021-12-29T15:59:44.582800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:44.654736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.09114
26.6%
3.06924
20.2%
2.06591
19.2%
4.05931
17.3%
1.05754
16.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
12918 
1.0
9222 
2.0
6470 
3.0
4764 
4.0
 
940

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.012918
30.1%
1.09222
21.5%
2.06470
15.1%
3.04764
 
11.1%
4.0940
 
2.2%
(Missing)8648
20.1%

Length

2021-12-29T15:59:44.744419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:44.807697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.012918
37.6%
1.09222
26.9%
2.06470
18.9%
3.04764
 
13.9%
4.0940
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
26001 
1.0
2907 
3.0
2707 
2.0
2699 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.026001
60.5%
1.02907
 
6.8%
3.02707
 
6.3%
2.02699
 
6.3%
(Missing)8648
 
20.1%

Length

2021-12-29T15:59:44.898804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:44.967677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.026001
75.8%
1.02907
 
8.5%
3.02707
 
7.9%
2.02699
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
29002 
2.0
 
2702
1.0
 
2610

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.029002
67.5%
2.02702
 
6.3%
1.02610
 
6.1%
(Missing)8648
 
20.1%

Length

2021-12-29T15:59:45.050819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:45.111592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.029002
84.5%
2.02702
 
7.9%
1.02610
 
7.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_AUTOQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.420644635
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:45.182482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.22936339
Coefficient of variation (CV)0.3593952371
Kurtosis3.365315051
Mean3.420644635
Median Absolute Deviation (MAD)1
Skewness0.6767172155
Sum117376
Variance1.511334344
MonotonicityNot monotonic
2021-12-29T15:59:45.272229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311545
26.9%
410923
25.4%
54982
11.6%
24211
 
9.8%
12270
 
5.3%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12270
 
5.3%
24211
 
9.8%
311545
26.9%
410923
25.4%
54982
11.6%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
54982
11.6%
410923
25.4%
311545
26.9%
24211
 
9.8%
12270
 
5.3%

KBA05_BAUMAX
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.220726234
Minimum0
Maximum5
Zeros14332
Zeros (%)33.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:45.365733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.58808825
Coefficient of variation (CV)1.300937267
Kurtosis0.7005542379
Mean1.220726234
Median Absolute Deviation (MAD)1
Skewness1.405282847
Sum41888
Variance2.522024291
MonotonicityNot monotonic
2021-12-29T15:59:45.457080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
014332
33.4%
112829
29.9%
53317
 
7.7%
32238
 
5.2%
41282
 
3.0%
2316
 
0.7%
(Missing)8648
20.1%
ValueCountFrequency (%)
014332
33.4%
112829
29.9%
2316
 
0.7%
32238
 
5.2%
41282
 
3.0%
53317
 
7.7%
ValueCountFrequency (%)
53317
 
7.7%
41282
 
3.0%
32238
 
5.2%
2316
 
0.7%
112829
29.9%
014332
33.4%

KBA05_CCM1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.992597774
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:45.537838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.204404722
Coefficient of variation (CV)0.402461277
Kurtosis5.289976708
Mean2.992597774
Median Absolute Deviation (MAD)1
Skewness1.315301334
Sum102688
Variance1.450590733
MonotonicityNot monotonic
2021-12-29T15:59:45.619232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313891
32.3%
28232
19.2%
46492
15.1%
12861
 
6.7%
52455
 
5.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12861
 
6.7%
28232
19.2%
313891
32.3%
46492
15.1%
52455
 
5.7%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52455
 
5.7%
46492
15.1%
313891
32.3%
28232
19.2%
12861
 
6.7%

KBA05_CCM2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.071049717
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:45.721002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.176554058
Coefficient of variation (CV)0.3831113678
Kurtosis5.605164202
Mean3.071049717
Median Absolute Deviation (MAD)1
Skewness1.299056115
Sum105380
Variance1.384279451
MonotonicityNot monotonic
2021-12-29T15:59:45.802531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314432
33.6%
47445
17.3%
27259
16.9%
12409
 
5.6%
52386
 
5.6%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12409
 
5.6%
27259
16.9%
314432
33.6%
47445
17.3%
52386
 
5.6%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52386
 
5.6%
47445
17.3%
314432
33.6%
27259
16.9%
12409
 
5.6%

KBA05_CCM3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.120329895
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:45.884769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.208367039
Coefficient of variation (CV)0.3872561812
Kurtosis4.689270463
Mean3.120329895
Median Absolute Deviation (MAD)1
Skewness1.13172801
Sum107071
Variance1.460150901
MonotonicityNot monotonic
2021-12-29T15:59:45.976743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313770
32.1%
47871
18.3%
26740
15.7%
52950
 
6.9%
12600
 
6.1%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12600
 
6.1%
26740
15.7%
313770
32.1%
47871
18.3%
52950
 
6.9%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52950
 
6.9%
47871
18.3%
313770
32.1%
26740
15.7%
12600
 
6.1%

KBA05_CCM4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.35317946
Minimum0
Maximum9
Zeros10887
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:46.069293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.439305462
Coefficient of variation (CV)1.063647139
Kurtosis7.038340461
Mean1.35317946
Median Absolute Deviation (MAD)1
Skewness1.965284843
Sum46433
Variance2.071600213
MonotonicityNot monotonic
2021-12-29T15:59:46.149831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
111002
25.6%
010887
25.3%
26253
14.6%
33678
 
8.6%
42111
 
4.9%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
010887
25.3%
111002
25.6%
26253
14.6%
33678
 
8.6%
42111
 
4.9%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42111
 
4.9%
33678
 
8.6%
26253
14.6%
111002
25.6%
010887
25.3%

KBA05_DIESEL
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.103456315
Minimum0
Maximum9
Zeros2354
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:46.250046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.249751295
Coefficient of variation (CV)0.5941417875
Kurtosis8.509008969
Mean2.103456315
Median Absolute Deviation (MAD)1
Skewness1.755831312
Sum72178
Variance1.561878299
MonotonicityNot monotonic
2021-12-29T15:59:46.323979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
213876
32.3%
37601
17.7%
17408
17.2%
42692
 
6.3%
02354
 
5.5%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
02354
 
5.5%
17408
17.2%
213876
32.3%
37601
17.7%
42692
 
6.3%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42692
 
6.3%
37601
17.7%
213876
32.3%
17408
17.2%
02354
 
5.5%

KBA05_FRAU
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.062481786
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:46.405573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.200161724
Coefficient of variation (CV)0.3918918733
Kurtosis5.107672919
Mean3.062481786
Median Absolute Deviation (MAD)1
Skewness1.25596147
Sum105086
Variance1.440388163
MonotonicityNot monotonic
2021-12-29T15:59:46.485537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314346
33.4%
27371
17.2%
46819
15.9%
52797
 
6.5%
12598
 
6.0%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12598
 
6.0%
27371
17.2%
314346
33.4%
46819
15.9%
52797
 
6.5%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52797
 
6.5%
46819
15.9%
314346
33.4%
27371
17.2%
12598
 
6.0%

KBA05_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
4.0
8867 
5.0
8590 
3.0
8305 
2.0
4702 
1.0
3850 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row5.0
3rd row5.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
4.08867
20.6%
5.08590
20.0%
3.08305
19.3%
2.04702
10.9%
1.03850
9.0%
(Missing)8648
20.1%

Length

2021-12-29T15:59:46.580244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:46.651061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.08867
25.8%
5.08590
25.0%
3.08305
24.2%
2.04702
13.7%
1.03850
11.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_HERST1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.508305648
Minimum0
Maximum9
Zeros2645
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:46.722276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.503743983
Coefficient of variation (CV)0.5995058795
Kurtosis2.406982689
Mean2.508305648
Median Absolute Deviation (MAD)1
Skewness0.8829167479
Sum86070
Variance2.261245968
MonotonicityNot monotonic
2021-12-29T15:59:46.812148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
210232
23.8%
38391
19.5%
15533
12.9%
44197
9.8%
52933
 
6.8%
02645
 
6.2%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
02645
 
6.2%
15533
12.9%
210232
23.8%
38391
19.5%
44197
9.8%
52933
 
6.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52933
 
6.8%
44197
9.8%
38391
19.5%
210232
23.8%
15533
12.9%
02645
 
6.2%

KBA05_HERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.088535292
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:46.895441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.180464334
Coefficient of variation (CV)0.3822084655
Kurtosis5.414501318
Mean3.088535292
Median Absolute Deviation (MAD)1
Skewness1.321144196
Sum105980
Variance1.393496045
MonotonicityNot monotonic
2021-12-29T15:59:46.976675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314278
33.2%
27653
17.8%
47119
16.6%
52759
 
6.4%
12122
 
4.9%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12122
 
4.9%
27653
17.8%
314278
33.2%
47119
16.6%
52759
 
6.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52759
 
6.4%
47119
16.6%
314278
33.2%
27653
17.8%
12122
 
4.9%

KBA05_HERST3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.939208486
Minimum0
Maximum9
Zeros600
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.067952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.258517322
Coefficient of variation (CV)0.4281823926
Kurtosis4.667198085
Mean2.939208486
Median Absolute Deviation (MAD)1
Skewness1.025855911
Sum100856
Variance1.58386585
MonotonicityNot monotonic
2021-12-29T15:59:47.160072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
313990
32.6%
27688
17.9%
46338
14.8%
12966
 
6.9%
52349
 
5.5%
0600
 
1.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
0600
 
1.4%
12966
 
6.9%
27688
17.9%
313990
32.6%
46338
14.8%
52349
 
5.5%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52349
 
5.5%
46338
14.8%
313990
32.6%
27688
17.9%
12966
 
6.9%
0600
 
1.4%

KBA05_HERST4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.888995745
Minimum0
Maximum9
Zeros1037
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.241461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.356748115
Coefficient of variation (CV)0.4696262074
Kurtosis3.240260139
Mean2.888995745
Median Absolute Deviation (MAD)1
Skewness0.7988984006
Sum99133
Variance1.840765447
MonotonicityNot monotonic
2021-12-29T15:59:47.333199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
312152
28.3%
27856
18.3%
46312
14.7%
13650
 
8.5%
52924
 
6.8%
01037
 
2.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
01037
 
2.4%
13650
 
8.5%
27856
18.3%
312152
28.3%
46312
14.7%
52924
 
6.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52924
 
6.8%
46312
14.7%
312152
28.3%
27856
18.3%
13650
 
8.5%
01037
 
2.4%

KBA05_HERST5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.872471877
Minimum0
Maximum9
Zeros1641
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.435402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.399151011
Coefficient of variation (CV)0.487089542
Kurtosis2.81747835
Mean2.872471877
Median Absolute Deviation (MAD)1
Skewness0.6308281944
Sum98566
Variance1.957623552
MonotonicityNot monotonic
2021-12-29T15:59:47.506794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
311303
26.3%
27812
18.2%
47113
16.6%
13294
 
7.7%
52768
 
6.4%
01641
 
3.8%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
01641
 
3.8%
13294
 
7.7%
27812
18.2%
311303
26.3%
47113
16.6%
52768
 
6.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52768
 
6.4%
47113
16.6%
311303
26.3%
27812
18.2%
13294
 
7.7%
01641
 
3.8%

KBA05_HERSTTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.674491971
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.596308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.344833271
Coefficient of variation (CV)0.5028369073
Kurtosis4.747922392
Mean2.674491971
Median Absolute Deviation (MAD)1
Skewness1.3770299
Sum94102
Variance1.808576528
MonotonicityNot monotonic
2021-12-29T15:59:47.700764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312167
28.3%
28114
18.9%
17461
17.4%
45211
12.1%
51755
 
4.1%
9477
 
1.1%
(Missing)7777
18.1%
ValueCountFrequency (%)
17461
17.4%
28114
18.9%
312167
28.3%
45211
12.1%
51755
 
4.1%
9477
 
1.1%
ValueCountFrequency (%)
9477
 
1.1%
51755
 
4.1%
45211
12.1%
312167
28.3%
28114
18.9%
17461
17.4%

KBA05_KRSAQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.285277146
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.801180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.241441684
Coefficient of variation (CV)0.3778803519
Kurtosis3.530424036
Mean3.285277146
Median Absolute Deviation (MAD)1
Skewness0.8832428082
Sum112731
Variance1.541177455
MonotonicityNot monotonic
2021-12-29T15:59:47.875064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313575
31.6%
48032
18.7%
25147
 
12.0%
54740
 
11.0%
12437
 
5.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12437
 
5.7%
25147
 
12.0%
313575
31.6%
48032
18.7%
54740
 
11.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
54740
 
11.0%
48032
18.7%
313575
31.6%
25147
 
12.0%
12437
 
5.7%

KBA05_KRSHERST1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.066998893
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:47.966887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.158735549
Coefficient of variation (CV)0.3778076189
Kurtosis6.029329833
Mean3.066998893
Median Absolute Deviation (MAD)1
Skewness1.36870094
Sum105241
Variance1.342668072
MonotonicityNot monotonic
2021-12-29T15:59:48.049080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314112
32.8%
47873
18.3%
27740
18.0%
12136
 
5.0%
52070
 
4.8%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12136
 
5.0%
27740
18.0%
314112
32.8%
47873
18.3%
52070
 
4.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52070
 
4.8%
47873
18.3%
314112
32.8%
27740
18.0%
12136
 
5.0%

KBA05_KRSHERST2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.065221193
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:48.141488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.206869309
Coefficient of variation (CV)0.3937299246
Kurtosis4.950031455
Mean3.065221193
Median Absolute Deviation (MAD)1
Skewness1.204069901
Sum105180
Variance1.456533529
MonotonicityNot monotonic
2021-12-29T15:59:48.223053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314134
32.9%
47155
16.7%
27129
16.6%
12778
 
6.5%
52735
 
6.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12778
 
6.5%
27129
16.6%
314134
32.9%
47155
16.7%
52735
 
6.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52735
 
6.4%
47155
16.7%
314134
32.9%
27129
16.6%
12778
 
6.5%

KBA05_KRSHERST3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.078247945
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:48.314849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.233392628
Coefficient of variation (CV)0.4006800782
Kurtosis4.351613266
Mean3.078247945
Median Absolute Deviation (MAD)1
Skewness1.154885203
Sum105627
Variance1.521257374
MonotonicityNot monotonic
2021-12-29T15:59:48.714517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313784
32.1%
27385
17.2%
46604
15.4%
53371
 
7.8%
12787
 
6.5%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12787
 
6.5%
27385
17.2%
313784
32.1%
46604
15.4%
53371
 
7.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
53371
 
7.8%
46604
15.4%
313784
32.1%
27385
17.2%
12787
 
6.5%

KBA05_KRSKLEIN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
20925 
1.0
6807 
3.0
6199 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.020925
48.7%
1.06807
 
15.8%
3.06199
 
14.4%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:48.816114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:48.885806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.020925
61.0%
1.06807
 
19.8%
3.06199
 
18.1%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSOBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
22210 
3.0
5904 
1.0
5817 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.022210
51.7%
3.05904
 
13.7%
1.05817
 
13.5%
9.0383
 
0.9%
(Missing)8648
 
20.1%

Length

2021-12-29T15:59:48.969676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:49.040839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.022210
64.7%
3.05904
 
17.2%
1.05817
 
17.0%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSVAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
23642 
3.0
5571 
1.0
4718 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.023642
55.0%
3.05571
 
13.0%
1.04718
 
11.0%
9.0383
 
0.9%
(Missing)8648
 
20.1%

Length

2021-12-29T15:59:49.122773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:49.194655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.023642
68.9%
3.05571
 
16.2%
1.04718
 
13.7%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KRSZUL
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
18370 
1.0
7959 
3.0
7602 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.018370
42.8%
1.07959
18.5%
3.07602
17.7%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:49.276676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:49.338186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.018370
53.5%
1.07959
23.2%
3.07602
22.2%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_KW1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.949350119
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:49.417551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.234019412
Coefficient of variation (CV)0.4184038387
Kurtosis4.812061078
Mean2.949350119
Median Absolute Deviation (MAD)1
Skewness1.208199952
Sum101204
Variance1.522803908
MonotonicityNot monotonic
2021-12-29T15:59:49.499494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313283
30.9%
27902
18.4%
46778
15.8%
13712
 
8.6%
52256
 
5.3%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
13712
 
8.6%
27902
18.4%
313283
30.9%
46778
15.8%
52256
 
5.3%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52256
 
5.3%
46778
15.8%
313283
30.9%
27902
18.4%
13712
 
8.6%

KBA05_KW2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.133036079
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:49.583307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.177704189
Coefficient of variation (CV)0.3758987
Kurtosis5.317248036
Mean3.133036079
Median Absolute Deviation (MAD)1
Skewness1.254119633
Sum107507
Variance1.386987157
MonotonicityNot monotonic
2021-12-29T15:59:49.664859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314676
34.2%
47507
17.5%
26696
15.6%
52890
 
6.7%
12162
 
5.0%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12162
 
5.0%
26696
15.6%
314676
34.2%
47507
17.5%
52890
 
6.7%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52890
 
6.7%
47507
17.5%
314676
34.2%
26696
15.6%
12162
 
5.0%

KBA05_KW3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.57341027
Minimum0
Maximum9
Zeros7676
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:49.756209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4323919
Coefficient of variation (CV)0.9103740628
Kurtosis6.209303021
Mean1.57341027
Median Absolute Deviation (MAD)1
Skewness1.765938853
Sum53990
Variance2.051746555
MonotonicityNot monotonic
2021-12-29T15:59:49.835172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
111685
27.2%
27738
18.0%
07676
17.9%
33946
 
9.2%
42886
 
6.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
07676
17.9%
111685
27.2%
27738
18.0%
33946
 
9.2%
42886
 
6.7%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42886
 
6.7%
33946
 
9.2%
27738
18.0%
111685
27.2%
07676
17.9%

KBA05_MAXAH
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.65693303
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:49.918211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.356138266
Coefficient of variation (CV)0.3708403339
Kurtosis0.9747647514
Mean3.65693303
Median Absolute Deviation (MAD)1
Skewness0.3895971997
Sum125484
Variance1.839110997
MonotonicityNot monotonic
2021-12-29T15:59:49.989184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
511962
27.8%
39345
21.8%
26325
14.7%
45081
11.8%
11218
 
2.8%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
11218
 
2.8%
26325
14.7%
39345
21.8%
45081
11.8%
511962
27.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
511962
27.8%
45081
11.8%
39345
21.8%
26325
14.7%
11218
 
2.8%

KBA05_MAXBJ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
10402 
4.0
9490 
2.0
8318 
3.0
5721 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.010402
24.2%
4.09490
22.1%
2.08318
19.4%
3.05721
13.3%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:50.099183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:50.173190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010402
30.3%
4.09490
27.7%
2.08318
24.2%
3.05721
16.7%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXHERST
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.775805794
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:50.265306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.294420547
Coefficient of variation (CV)0.4663224459
Kurtosis4.281331029
Mean2.775805794
Median Absolute Deviation (MAD)1
Skewness1.416249411
Sum95249
Variance1.675524552
MonotonicityNot monotonic
2021-12-29T15:59:50.346666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
212905
30.0%
39109
21.2%
45048
 
11.7%
13968
 
9.2%
52901
 
6.8%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
13968
 
9.2%
212905
30.0%
39109
21.2%
45048
 
11.7%
52901
 
6.8%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52901
 
6.8%
45048
 
11.7%
39109
21.2%
212905
30.0%
13968
 
9.2%

KBA05_MAXSEG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
14241 
1.0
8786 
3.0
7710 
4.0
3194 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.014241
33.1%
1.08786
20.5%
3.07710
17.9%
4.03194
 
7.4%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:50.449015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:50.520717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.014241
41.5%
1.08786
25.6%
3.07710
22.5%
4.03194
 
9.3%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MAXVORB
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
2.0
15978 
1.0
9898 
3.0
8055 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.015978
37.2%
1.09898
23.0%
3.08055
18.7%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:50.602820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:50.684570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.015978
46.6%
1.09898
28.8%
3.08055
23.5%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOD1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.446231859
Minimum0
Maximum9
Zeros11398
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:50.766636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.469448114
Coefficient of variation (CV)1.016052928
Kurtosis5.841506039
Mean1.446231859
Median Absolute Deviation (MAD)1
Skewness1.682487668
Sum49626
Variance2.15927776
MonotonicityNot monotonic
2021-12-29T15:59:50.838267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
011398
26.5%
28755
20.4%
17412
17.3%
34207
 
9.8%
42159
 
5.0%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
011398
26.5%
17412
17.3%
28755
20.4%
34207
 
9.8%
42159
 
5.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42159
 
5.0%
34207
 
9.8%
28755
20.4%
17412
17.3%
011398
26.5%

KBA05_MOD2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.066241184
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:50.919973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.181370091
Coefficient of variation (CV)0.3852828333
Kurtosis5.5058198
Mean3.066241184
Median Absolute Deviation (MAD)1
Skewness1.277101221
Sum105215
Variance1.395635292
MonotonicityNot monotonic
2021-12-29T15:59:51.001408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314227
33.1%
47570
17.6%
27285
17.0%
12502
 
5.8%
52347
 
5.5%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12502
 
5.8%
27285
17.0%
314227
33.1%
47570
17.6%
52347
 
5.5%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52347
 
5.5%
47570
17.6%
314227
33.1%
27285
17.0%
12502
 
5.8%

KBA05_MOD3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.076382818
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:51.092462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.202863338
Coefficient of variation (CV)0.3909992382
Kurtosis4.930894176
Mean3.076382818
Median Absolute Deviation (MAD)1
Skewness1.20675574
Sum105563
Variance1.44688021
MonotonicityNot monotonic
2021-12-29T15:59:51.184265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313109
30.5%
47966
18.5%
27777
18.1%
52573
 
6.0%
12506
 
5.8%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12506
 
5.8%
27777
18.1%
313109
30.5%
47966
18.5%
52573
 
6.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52573
 
6.0%
47966
18.5%
313109
30.5%
27777
18.1%
12506
 
5.8%

KBA05_MOD4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.781080608
Minimum0
Maximum9
Zeros1672
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:51.285378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.45132989
Coefficient of variation (CV)0.5218582612
Kurtosis2.338874769
Mean2.781080608
Median Absolute Deviation (MAD)1
Skewness0.6805024773
Sum95430
Variance2.106358451
MonotonicityNot monotonic
2021-12-29T15:59:51.364812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
310713
24.9%
27707
17.9%
45993
13.9%
14693
10.9%
53153
 
7.3%
01672
 
3.9%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
01672
 
3.9%
14693
10.9%
27707
17.9%
310713
24.9%
45993
13.9%
53153
 
7.3%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
53153
 
7.3%
45993
13.9%
310713
24.9%
27707
17.9%
14693
10.9%
01672
 
3.9%

KBA05_MOD8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
11159 
2.0
10321 
0.0
8542 
3.0
3909 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.011159
26.0%
2.010321
24.0%
0.08542
19.9%
3.03909
 
9.1%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:51.455998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:51.538929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.011159
32.5%
2.010321
30.1%
0.08542
24.9%
3.03909
 
11.4%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MODTEMP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.917947989
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:51.618686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.198852574
Coefficient of variation (CV)0.4108546755
Kurtosis-0.689624471
Mean2.917947989
Median Absolute Deviation (MAD)1
Skewness-0.2299547074
Sum102668
Variance1.437247494
MonotonicityNot monotonic
2021-12-29T15:59:51.700555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312430
28.9%
410139
23.6%
16827
15.9%
23746
 
8.7%
51755
 
4.1%
6288
 
0.7%
(Missing)7777
18.1%
ValueCountFrequency (%)
16827
15.9%
23746
 
8.7%
312430
28.9%
410139
23.6%
51755
 
4.1%
6288
 
0.7%
ValueCountFrequency (%)
6288
 
0.7%
51755
 
4.1%
410139
23.6%
312430
28.9%
23746
 
8.7%
16827
15.9%

KBA05_MOTOR
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
3.0
13689 
2.0
10039 
4.0
5401 
1.0
4802 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.013689
31.9%
2.010039
23.4%
4.05401
 
12.6%
1.04802
 
11.2%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:51.794492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:51.865470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013689
39.9%
2.010039
29.3%
4.05401
 
15.7%
1.04802
 
14.0%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_MOTRAD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
18109 
0.0
8023 
3.0
4112 
2.0
3723 
9.0
 
347

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.018109
42.2%
0.08023
18.7%
3.04112
 
9.6%
2.03723
 
8.7%
9.0347
 
0.8%
(Missing)8648
20.1%

Length

2021-12-29T15:59:51.957481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:52.038310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.018109
52.8%
0.08023
23.4%
3.04112
 
12.0%
2.03723
 
10.8%
9.0347
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
12891 
0.0
10321 
2.0
8469 
3.0
2250 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.012891
30.0%
0.010321
24.0%
2.08469
19.7%
3.02250
 
5.2%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:52.130250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:52.201604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.012891
37.6%
0.010321
30.1%
2.08469
24.7%
3.02250
 
6.6%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG10
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.001253133
Minimum0
Maximum9
Zeros3972
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:52.282919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.325463855
Coefficient of variation (CV)0.6623169421
Kurtosis6.840764292
Mean2.001253133
Median Absolute Deviation (MAD)1
Skewness1.522291599
Sum68671
Variance1.756854432
MonotonicityNot monotonic
2021-12-29T15:59:52.374954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
212809
29.8%
17335
17.1%
36989
16.3%
03972
 
9.2%
42826
 
6.6%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
03972
 
9.2%
17335
17.1%
212809
29.8%
36989
16.3%
42826
 
6.6%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42826
 
6.6%
36989
16.3%
212809
29.8%
17335
17.1%
03972
 
9.2%

KBA05_SEG2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.028618057
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:52.456917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.184516939
Coefficient of variation (CV)0.3911080622
Kurtosis5.61446224
Mean3.028618057
Median Absolute Deviation (MAD)1
Skewness1.28784234
Sum103924
Variance1.40308038
MonotonicityNot monotonic
2021-12-29T15:59:52.539003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314475
33.7%
27258
16.9%
47222
16.8%
12808
 
6.5%
52168
 
5.0%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12808
 
6.5%
27258
16.9%
314475
33.7%
47222
16.8%
52168
 
5.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52168
 
5.0%
47222
16.8%
314475
33.7%
27258
16.9%
12808
 
6.5%

KBA05_SEG3
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.053709856
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:52.629079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.208823446
Coefficient of variation (CV)0.3958540604
Kurtosis4.881259485
Mean3.053709856
Median Absolute Deviation (MAD)1
Skewness1.243400618
Sum104785
Variance1.461254123
MonotonicityNot monotonic
2021-12-29T15:59:52.702816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312879
30.0%
28382
19.5%
47537
17.5%
52664
 
6.2%
12469
 
5.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12469
 
5.7%
28382
19.5%
312879
30.0%
47537
17.5%
52664
 
6.2%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52664
 
6.2%
47537
17.5%
312879
30.0%
28382
19.5%
12469
 
5.7%

KBA05_SEG4
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.082765052
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:52.794729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.166556021
Coefficient of variation (CV)0.3784122374
Kurtosis5.848310393
Mean3.082765052
Median Absolute Deviation (MAD)1
Skewness1.341649132
Sum105782
Variance1.360852949
MonotonicityNot monotonic
2021-12-29T15:59:52.874319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
315486
36.0%
46807
15.8%
26739
15.7%
52568
 
6.0%
12331
 
5.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12331
 
5.4%
26739
15.7%
315486
36.0%
46807
15.8%
52568
 
6.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52568
 
6.0%
46807
15.8%
315486
36.0%
26739
15.7%
12331
 
5.4%

KBA05_SEG5
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.585941598
Minimum0
Maximum9
Zeros7092
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:52.968282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.390973669
Coefficient of variation (CV)0.8770648745
Kurtosis7.119229204
Mean1.585941598
Median Absolute Deviation (MAD)1
Skewness1.849411219
Sum54420
Variance1.934807747
MonotonicityNot monotonic
2021-12-29T15:59:53.051649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
111597
27.0%
28674
20.2%
07092
16.5%
34244
 
9.9%
42324
 
5.4%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
07092
16.5%
111597
27.0%
28674
20.2%
34244
 
9.9%
42324
 
5.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
42324
 
5.4%
34244
 
9.9%
28674
20.2%
111597
27.0%
07092
16.5%

KBA05_SEG6
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
29474 
1.0
4457 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.029474
68.6%
1.04457
 
10.4%
9.0383
 
0.9%
(Missing)8648
 
20.1%

Length

2021-12-29T15:59:53.151394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:53.220705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.029474
85.9%
1.04457
 
13.0%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG7
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
15246 
1.0
9617 
2.0
6799 
3.0
2269 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.015246
35.5%
1.09617
22.4%
2.06799
15.8%
3.02269
 
5.3%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:53.293668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:53.362852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.015246
44.4%
1.09617
28.0%
2.06799
19.8%
3.02269
 
6.6%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG8
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
0.0
17259 
1.0
9149 
2.0
5627 
3.0
1896 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.017259
40.2%
1.09149
21.3%
2.05627
 
13.1%
3.01896
 
4.4%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:53.445829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:53.517183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.017259
50.3%
1.09149
26.7%
2.05627
 
16.4%
3.01896
 
5.5%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_SEG9
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Memory size335.8 KiB
1.0
12306 
0.0
10318 
2.0
8891 
3.0
2416 
9.0
 
383

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row2.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.012306
28.6%
0.010318
24.0%
2.08891
20.7%
3.02416
 
5.6%
9.0383
 
0.9%
(Missing)8648
20.1%

Length

2021-12-29T15:59:53.608614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:53.680091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.012306
35.9%
0.010318
30.1%
2.08891
25.9%
3.02416
 
7.0%
9.0383
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA05_VORB0
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.142536574
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:53.751727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.24960115
Coefficient of variation (CV)0.397640925
Kurtosis3.771401199
Mean3.142536574
Median Absolute Deviation (MAD)1
Skewness0.9708559709
Sum107833
Variance1.561503034
MonotonicityNot monotonic
2021-12-29T15:59:53.831350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311742
27.3%
48893
20.7%
27196
16.7%
53274
 
7.6%
12826
 
6.6%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12826
 
6.6%
27196
16.7%
311742
27.3%
48893
20.7%
53274
 
7.6%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
53274
 
7.6%
48893
20.7%
311742
27.3%
27196
16.7%
12826
 
6.6%

KBA05_VORB1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.066153756
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:53.915341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.190798015
Coefficient of variation (CV)0.3883686564
Kurtosis5.321383657
Mean3.066153756
Median Absolute Deviation (MAD)1
Skewness1.269399122
Sum105212
Variance1.417999912
MonotonicityNot monotonic
2021-12-29T15:59:53.996843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314762
34.4%
27042
16.4%
46844
15.9%
52684
 
6.2%
12599
 
6.0%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12599
 
6.0%
27042
16.4%
314762
34.4%
46844
15.9%
52684
 
6.2%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52684
 
6.2%
46844
15.9%
314762
34.4%
27042
16.4%
12599
 
6.0%

KBA05_VORB2
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.526024363
Minimum0
Maximum9
Zeros2781
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:54.117198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.450604629
Coefficient of variation (CV)0.5742639104
Kurtosis2.989646207
Mean2.526024363
Median Absolute Deviation (MAD)1
Skewness0.8214422007
Sum86678
Variance2.104253788
MonotonicityNot monotonic
2021-12-29T15:59:54.190403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
310839
25.2%
28849
20.6%
14940
11.5%
44534
10.6%
02781
 
6.5%
51988
 
4.6%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
02781
 
6.5%
14940
11.5%
28849
20.6%
310839
25.2%
44534
10.6%
51988
 
4.6%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
51988
 
4.6%
44534
10.6%
310839
25.2%
28849
20.6%
14940
11.5%
02781
 
6.5%

KBA05_ZUL1
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.920557207
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:54.281907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.180197785
Coefficient of variation (CV)0.4041002116
Kurtosis6.205794046
Mean2.920557207
Median Absolute Deviation (MAD)1
Skewness1.396424703
Sum100216
Variance1.392866813
MonotonicityNot monotonic
2021-12-29T15:59:54.360906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314442
33.6%
28076
18.8%
46502
15.1%
13318
 
7.7%
51593
 
3.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
13318
 
7.7%
28076
18.8%
314442
33.6%
46502
15.1%
51593
 
3.7%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
51593
 
3.7%
46502
15.1%
314442
33.6%
28076
18.8%
13318
 
7.7%

KBA05_ZUL2
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.109459696
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:54.454037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.185905892
Coefficient of variation (CV)0.3813864814
Kurtosis5.191812642
Mean3.109459696
Median Absolute Deviation (MAD)1
Skewness1.248697912
Sum106698
Variance1.406372785
MonotonicityNot monotonic
2021-12-29T15:59:54.543247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313898
32.3%
47744
18.0%
27340
17.1%
52738
 
6.4%
12211
 
5.1%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
12211
 
5.1%
27340
17.1%
313898
32.3%
47744
18.0%
52738
 
6.4%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52738
 
6.4%
47744
18.0%
313898
32.3%
27340
17.1%
12211
 
5.1%

KBA05_ZUL3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.857783995
Minimum0
Maximum9
Zeros2023
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:54.636903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.423313285
Coefficient of variation (CV)0.4980478886
Kurtosis2.605736653
Mean2.857783995
Median Absolute Deviation (MAD)1
Skewness0.5349743533
Sum98062
Variance2.025820706
MonotonicityNot monotonic
2021-12-29T15:59:54.717947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
310995
25.6%
47657
17.8%
27490
17.4%
13202
 
7.5%
52564
 
6.0%
02023
 
4.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
02023
 
4.7%
13202
 
7.5%
27490
17.4%
310995
25.6%
47657
17.8%
52564
 
6.0%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52564
 
6.0%
47657
17.8%
310995
25.6%
27490
17.4%
13202
 
7.5%
02023
 
4.7%

KBA05_ZUL4
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.342833829
Minimum0
Maximum9
Zeros3181
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:54.809213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.567759636
Coefficient of variation (CV)0.6691723575
Kurtosis2.004025628
Mean2.342833829
Median Absolute Deviation (MAD)1
Skewness0.9298431483
Sum80392
Variance2.457870278
MonotonicityNot monotonic
2021-12-29T15:59:54.890475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
28894
20.7%
18224
19.1%
36051
14.1%
45125
11.9%
03181
 
7.4%
52456
 
5.7%
9383
 
0.9%
(Missing)8648
20.1%
ValueCountFrequency (%)
03181
 
7.4%
18224
19.1%
28894
20.7%
36051
14.1%
45125
11.9%
52456
 
5.7%
9383
 
0.9%
ValueCountFrequency (%)
9383
 
0.9%
52456
 
5.7%
45125
11.9%
36051
14.1%
28894
20.7%
18224
19.1%
03181
 
7.4%

KBA13_ALTERHALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14704 
2.0
7463 
4.0
6280 
1.0
3853 
5.0
2700 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row5.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014704
34.2%
2.07463
17.4%
4.06280
14.6%
1.03853
 
9.0%
5.02700
 
6.3%
(Missing)7962
18.5%

Length

2021-12-29T15:59:54.990447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.052025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014704
42.0%
2.07463
21.3%
4.06280
17.9%
1.03853
 
11.0%
5.02700
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13741 
4.0
7185 
2.0
7037 
1.0
3540 
5.0
3497 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.013741
32.0%
4.07185
16.7%
2.07037
16.4%
1.03540
 
8.2%
5.03497
 
8.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.145357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.214202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013741
39.3%
4.07185
20.5%
2.07037
20.1%
1.03540
 
10.1%
5.03497
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14051 
2.0
8206 
4.0
6355 
1.0
3614 
5.0
2774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014051
32.7%
2.08206
19.1%
4.06355
14.8%
1.03614
 
8.4%
5.02774
 
6.5%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.297983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.377445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014051
40.1%
2.08206
23.4%
4.06355
18.2%
1.03614
 
10.3%
5.02774
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ALTERHALTER_61
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13661 
4.0
7948 
2.0
6321 
5.0
4447 
1.0
2623 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.013661
31.8%
4.07948
18.5%
2.06321
14.7%
5.04447
 
10.4%
1.02623
 
6.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.471458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.542312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013661
39.0%
4.07948
22.7%
2.06321
18.1%
5.04447
 
12.7%
1.02623
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
2.0
13088 
3.0
11072 
1.0
7256 
4.0
3244 
0.0
 
340

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.013088
30.5%
3.011072
25.8%
1.07256
16.9%
4.03244
 
7.6%
0.0340
 
0.8%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.623233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.705211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.013088
37.4%
3.011072
31.6%
1.07256
20.7%
4.03244
 
9.3%
0.0340
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14698 
2.0
9988 
4.0
6802 
1.0
3023 
0.0
 
489

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014698
34.2%
2.09988
23.2%
4.06802
15.8%
1.03023
 
7.0%
0.0489
 
1.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.797235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:55.866767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014698
42.0%
2.09988
28.5%
4.06802
19.4%
1.03023
 
8.6%
0.0489
 
1.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
10716 
2.0
10442 
0.0
7120 
3.0
6722 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010716
24.9%
2.010442
24.3%
0.07120
16.6%
3.06722
15.6%
(Missing)7962
18.5%

Length

2021-12-29T15:59:55.959714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:56.020469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.010716
30.6%
2.010442
29.8%
0.07120
20.3%
3.06722
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
0.0
18785 
1.0
11393 
2.0
4822 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.018785
43.7%
1.011393
26.5%
2.04822
 
11.2%
(Missing)7962
18.5%

Length

2021-12-29T15:59:56.110230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:56.183843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.018785
53.7%
1.011393
32.6%
2.04822
 
13.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_ANZAHL_PKW
Real number (ℝ≥0)

MISSING

Distinct1230
Distinct (%)3.5%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean620.8817143
Minimum0
Maximum2300
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:56.285990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile206
Q1389
median549
Q3773
95-th percentile1300
Maximum2300
Range2300
Interquartile range (IQR)384

Descriptive statistics

Standard deviation338.5713782
Coefficient of variation (CV)0.5453073756
Kurtosis2.203107266
Mean620.8817143
Median Absolute Deviation (MAD)183.5
Skewness1.31044492
Sum21730860
Variance114630.5781
MonotonicityNot monotonic
2021-12-29T15:59:56.408184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400494
 
1.1%
1500382
 
0.9%
1300295
 
0.7%
1600282
 
0.7%
1700154
 
0.4%
1800135
 
0.3%
190088
 
0.2%
51785
 
0.2%
53482
 
0.2%
37778
 
0.2%
Other values (1220)32925
76.6%
(Missing)7962
 
18.5%
ValueCountFrequency (%)
04
< 0.1%
21
 
< 0.1%
41
 
< 0.1%
151
 
< 0.1%
252
< 0.1%
261
 
< 0.1%
272
< 0.1%
312
< 0.1%
321
 
< 0.1%
331
 
< 0.1%
ValueCountFrequency (%)
230025
 
0.1%
220015
 
< 0.1%
210024
 
0.1%
200053
 
0.1%
190088
 
0.2%
1800135
 
0.3%
1700154
 
0.4%
1600282
0.7%
1500382
0.9%
1400494
1.1%

KBA13_AUDI
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15198 
4.0
7859 
2.0
6455 
5.0
3311 
1.0
2177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row3.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015198
35.4%
4.07859
18.3%
2.06455
15.0%
5.03311
 
7.7%
1.02177
 
5.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:56.519886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:56.590723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015198
43.4%
4.07859
22.5%
2.06455
18.4%
5.03311
 
9.5%
1.02177
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_AUTOQUOTE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14561 
2.0
7827 
4.0
6569 
1.0
3468 
5.0
2575 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014561
33.9%
2.07827
18.2%
4.06569
15.3%
1.03468
 
8.1%
5.02575
 
6.0%
(Missing)7962
18.5%

Length

2021-12-29T15:59:56.691910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:56.762795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014561
41.6%
2.07827
22.4%
4.06569
18.8%
1.03468
 
9.9%
5.02575
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
23735 
5.0
4155 
2.0
2808 
3.0
 
2311
4.0
 
1991

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.023735
55.2%
5.04155
 
9.7%
2.02808
 
6.5%
3.02311
 
5.4%
4.01991
 
4.6%
(Missing)7962
 
18.5%

Length

2021-12-29T15:59:56.844060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:57.343488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.023735
67.8%
5.04155
 
11.9%
2.02808
 
8.0%
3.02311
 
6.6%
4.01991
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_1999
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15749 
2.0
8256 
4.0
6429 
1.0
2683 
5.0
1883 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.015749
36.7%
2.08256
19.2%
4.06429
15.0%
1.02683
 
6.2%
5.01883
 
4.4%
(Missing)7962
18.5%

Length

2021-12-29T15:59:57.435069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:57.504354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015749
45.0%
2.08256
23.6%
4.06429
18.4%
1.02683
 
7.7%
5.01883
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15568 
2.0
8461 
4.0
5944 
1.0
3274 
5.0
1753 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.015568
36.2%
2.08461
19.7%
4.05944
 
13.8%
1.03274
 
7.6%
5.01753
 
4.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:57.588345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:57.660263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015568
44.5%
2.08461
24.2%
4.05944
 
17.0%
1.03274
 
9.4%
5.01753
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2004
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16146 
4.0
7459 
2.0
7026 
5.0
2429 
1.0
1940 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.016146
37.6%
4.07459
17.4%
2.07026
16.4%
5.02429
 
5.7%
1.01940
 
4.5%
(Missing)7962
18.5%

Length

2021-12-29T15:59:57.750472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:57.814054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016146
46.1%
4.07459
21.3%
2.07026
20.1%
5.02429
 
6.9%
1.01940
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2006
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15993 
4.0
8016 
2.0
6674 
5.0
2531 
1.0
1786 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015993
37.2%
4.08016
18.7%
2.06674
15.5%
5.02531
 
5.9%
1.01786
 
4.2%
(Missing)7962
18.5%

Length

2021-12-29T15:59:57.905437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:57.976708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015993
45.7%
4.08016
22.9%
2.06674
19.1%
5.02531
 
7.2%
1.01786
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_BJ_2008
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.5298
Minimum0
Maximum5
Zeros5588
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:58.048324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.480473963
Coefficient of variation (CV)0.5852138362
Kurtosis-0.6424726231
Mean2.5298
Median Absolute Deviation (MAD)1
Skewness-0.2665339188
Sum88543
Variance2.191803154
MonotonicityNot monotonic
2021-12-29T15:59:58.130086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312748
29.7%
26672
15.5%
05588
13.0%
44161
 
9.7%
53620
 
8.4%
12211
 
5.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
05588
13.0%
12211
 
5.1%
26672
15.5%
312748
29.7%
44161
 
9.7%
53620
 
8.4%
ValueCountFrequency (%)
53620
 
8.4%
44161
 
9.7%
312748
29.7%
26672
15.5%
12211
 
5.1%
05588
13.0%

KBA13_BJ_2009
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.561742857
Minimum0
Maximum5
Zeros4183
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:58.221893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.460417812
Coefficient of variation (CV)0.5700875902
Kurtosis-0.7233699208
Mean2.561742857
Median Absolute Deviation (MAD)1
Skewness-0.1866605002
Sum89661
Variance2.132820186
MonotonicityNot monotonic
2021-12-29T15:59:58.311635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312814
29.8%
25159
12.0%
14762
 
11.1%
44271
 
9.9%
04183
 
9.7%
53811
 
8.9%
(Missing)7962
18.5%
ValueCountFrequency (%)
04183
 
9.7%
14762
 
11.1%
25159
12.0%
312814
29.8%
44271
 
9.9%
53811
 
8.9%
ValueCountFrequency (%)
53811
 
8.9%
44271
 
9.9%
312814
29.8%
25159
12.0%
14762
 
11.1%
04183
 
9.7%

KBA13_BMW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14866 
4.0
8306 
2.0
6076 
5.0
4016 
1.0
1736 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row3.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014866
34.6%
4.08306
19.3%
2.06076
14.1%
5.04016
 
9.3%
1.01736
 
4.0%
(Missing)7962
18.5%

Length

2021-12-29T15:59:58.416117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:58.485323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014866
42.5%
4.08306
23.7%
2.06076
17.4%
5.04016
 
11.5%
1.01736
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_0_1400
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.238342857
Minimum0
Maximum5
Zeros6654
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:58.558593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.438429828
Coefficient of variation (CV)0.6426315895
Kurtosis-0.7262770607
Mean2.238342857
Median Absolute Deviation (MAD)1
Skewness-0.09732439064
Sum78342
Variance2.06908037
MonotonicityNot monotonic
2021-12-29T15:59:58.637615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311406
26.5%
28383
19.5%
06654
15.5%
43259
 
7.6%
13042
 
7.1%
52256
 
5.3%
(Missing)7962
18.5%
ValueCountFrequency (%)
06654
15.5%
13042
 
7.1%
28383
19.5%
311406
26.5%
43259
 
7.6%
52256
 
5.3%
ValueCountFrequency (%)
52256
 
5.3%
43259
 
7.6%
311406
26.5%
28383
19.5%
13042
 
7.1%
06654
15.5%

KBA13_CCM_1000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.348085714
Minimum0
Maximum5
Zeros4518
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:58.730887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.407228807
Coefficient of variation (CV)0.5993089598
Kurtosis-0.7095739012
Mean2.348085714
Median Absolute Deviation (MAD)1
Skewness-0.0579613174
Sum82183
Variance1.980292915
MonotonicityNot monotonic
2021-12-29T15:59:58.811826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312626
29.4%
26007
14.0%
15915
13.8%
04518
 
10.5%
43294
 
7.7%
52640
 
6.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
04518
 
10.5%
15915
13.8%
26007
14.0%
312626
29.4%
43294
 
7.7%
52640
 
6.1%
ValueCountFrequency (%)
52640
 
6.1%
43294
 
7.7%
312626
29.4%
26007
14.0%
15915
13.8%
04518
 
10.5%

KBA13_CCM_1200
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.260942857
Minimum0
Maximum5
Zeros6800
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:58.892903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.457021735
Coefficient of variation (CV)0.6444310305
Kurtosis-0.7821499147
Mean2.260942857
Median Absolute Deviation (MAD)1
Skewness-0.1358785767
Sum79133
Variance2.122912337
MonotonicityNot monotonic
2021-12-29T15:59:58.981975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312036
28.0%
27389
17.2%
06800
15.8%
43424
 
8.0%
13051
 
7.1%
52300
 
5.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
06800
15.8%
13051
 
7.1%
27389
17.2%
312036
28.0%
43424
 
8.0%
52300
 
5.4%
ValueCountFrequency (%)
52300
 
5.4%
43424
 
8.0%
312036
28.0%
27389
17.2%
13051
 
7.1%
06800
15.8%

KBA13_CCM_1400
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16139 
2.0
7594 
4.0
6878 
5.0
2218 
1.0
2171 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.016139
37.6%
2.07594
17.7%
4.06878
16.0%
5.02218
 
5.2%
1.02171
 
5.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:59.075794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:59.157898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016139
46.1%
2.07594
21.7%
4.06878
19.7%
5.02218
 
6.3%
1.02171
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1401_2500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16012 
4.0
7666 
2.0
7097 
1.0
2469 
5.0
1756 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.016012
37.3%
4.07666
17.8%
2.07097
16.5%
1.02469
 
5.7%
5.01756
 
4.1%
(Missing)7962
18.5%

Length

2021-12-29T15:59:59.260423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:59.332052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016012
45.7%
4.07666
21.9%
2.07097
20.3%
1.02469
 
7.1%
5.01756
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1500
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
12620 
4.0
9074 
3.0
7174 
5.0
3107 
2.0
3025 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.012620
29.4%
4.09074
21.1%
3.07174
16.7%
5.03107
 
7.2%
2.03025
 
7.0%
(Missing)7962
18.5%

Length

2021-12-29T15:59:59.432358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:59.495707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.012620
36.1%
4.09074
25.9%
3.07174
20.5%
5.03107
 
8.9%
2.03025
 
8.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1600
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15983 
2.0
7488 
4.0
7273 
5.0
2467 
1.0
1789 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015983
37.2%
2.07488
17.4%
4.07273
16.9%
5.02467
 
5.7%
1.01789
 
4.2%
(Missing)7962
18.5%

Length

2021-12-29T15:59:59.587181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T15:59:59.659005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015983
45.7%
2.07488
21.4%
4.07273
20.8%
5.02467
 
7.0%
1.01789
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_1800
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.3902
Minimum0
Maximum5
Zeros5981
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T15:59:59.741084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.438035431
Coefficient of variation (CV)0.6016381186
Kurtosis-0.6317465486
Mean2.3902
Median Absolute Deviation (MAD)1
Skewness-0.2170560403
Sum83657
Variance2.067945901
MonotonicityNot monotonic
2021-12-29T15:59:59.841291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312415
28.9%
27835
18.2%
05981
13.9%
43783
 
8.8%
52656
 
6.2%
12330
 
5.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
05981
13.9%
12330
 
5.4%
27835
18.2%
312415
28.9%
43783
 
8.8%
52656
 
6.2%
ValueCountFrequency (%)
52656
 
6.2%
43783
 
8.8%
312415
28.9%
27835
18.2%
12330
 
5.4%
05981
13.9%

KBA13_CCM_2000
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15914 
4.0
8040 
2.0
6709 
5.0
2902 
1.0
 
1435

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row5.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015914
37.0%
4.08040
18.7%
2.06709
15.6%
5.02902
 
6.8%
1.01435
 
3.3%
(Missing)7962
18.5%

Length

2021-12-29T15:59:59.934999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:00.027206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015914
45.5%
4.08040
23.0%
2.06709
19.2%
5.02902
 
8.3%
1.01435
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_CCM_2500
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.505171429
Minimum0
Maximum5
Zeros4153
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:00.106667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.425814022
Coefficient of variation (CV)0.569148285
Kurtosis-0.6608483219
Mean2.505171429
Median Absolute Deviation (MAD)1
Skewness-0.1504066803
Sum87681
Variance2.032945626
MonotonicityNot monotonic
2021-12-29T16:00:00.179650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312608
29.3%
26177
14.4%
14695
 
10.9%
04153
 
9.7%
44027
 
9.4%
53340
 
7.8%
(Missing)7962
18.5%
ValueCountFrequency (%)
04153
 
9.7%
14695
 
10.9%
26177
14.4%
312608
29.3%
44027
 
9.4%
53340
 
7.8%
ValueCountFrequency (%)
53340
 
7.8%
44027
 
9.4%
312608
29.3%
26177
14.4%
14695
 
10.9%
04153
 
9.7%

KBA13_CCM_2501
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.532428571
Minimum0
Maximum5
Zeros3906
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:00.262057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.443718195
Coefficient of variation (CV)0.5700923655
Kurtosis-0.7309348543
Mean2.532428571
Median Absolute Deviation (MAD)1
Skewness-0.1429879731
Sum88635
Variance2.084322226
MonotonicityNot monotonic
2021-12-29T16:00:00.344127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312818
29.8%
15433
12.6%
25124
 
11.9%
44095
 
9.5%
03906
 
9.1%
53624
 
8.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
03906
 
9.1%
15433
12.6%
25124
 
11.9%
312818
29.8%
44095
 
9.5%
53624
 
8.4%
ValueCountFrequency (%)
53624
 
8.4%
44095
 
9.5%
312818
29.8%
25124
 
11.9%
15433
12.6%
03906
 
9.1%

KBA13_CCM_3000
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.593257143
Minimum0
Maximum5
Zeros2500
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:00.425597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.375910107
Coefficient of variation (CV)0.5305721845
Kurtosis-0.6676067917
Mean2.593257143
Median Absolute Deviation (MAD)1
Skewness-0.09617638692
Sum90764
Variance1.893128623
MonotonicityNot monotonic
2021-12-29T16:00:00.506663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
313324
31.0%
16540
15.2%
24938
 
11.5%
44114
 
9.6%
53584
 
8.3%
02500
 
5.8%
(Missing)7962
18.5%
ValueCountFrequency (%)
02500
 
5.8%
16540
15.2%
24938
 
11.5%
313324
31.0%
44114
 
9.6%
53584
 
8.3%
ValueCountFrequency (%)
53584
 
8.3%
44114
 
9.6%
313324
31.0%
24938
 
11.5%
16540
15.2%
02500
 
5.8%

KBA13_CCM_3001
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
15236 
4.0
9418 
3.0
6585 
5.0
3758 
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.015236
35.5%
4.09418
21.9%
3.06585
15.3%
5.03758
 
8.7%
2.03
 
< 0.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:00.616379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:00.689505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.015236
43.5%
4.09418
26.9%
3.06585
18.8%
5.03758
 
10.7%
2.03
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14870 
2.0
7734 
4.0
6619 
1.0
3046 
5.0
2731 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014870
34.6%
2.07734
18.0%
4.06619
15.4%
1.03046
 
7.1%
5.02731
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:00.770833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:00.841749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014870
42.5%
2.07734
22.1%
4.06619
18.9%
1.03046
 
8.7%
5.02731
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FAB_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15033 
2.0
7544 
4.0
6905 
5.0
2764 
1.0
2754 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015033
35.0%
2.07544
17.6%
4.06905
16.1%
5.02764
 
6.4%
1.02754
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:00.943435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.014374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015033
43.0%
2.07544
21.6%
4.06905
19.7%
5.02764
 
7.9%
1.02754
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FIAT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14963 
4.0
7461 
2.0
7014 
5.0
3469 
1.0
2093 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014963
34.8%
4.07461
17.4%
2.07014
16.3%
5.03469
 
8.1%
1.02093
 
4.9%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.106171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.187393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014963
42.8%
4.07461
21.3%
2.07014
20.0%
5.03469
 
9.9%
1.02093
 
6.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_FORD
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14489 
2.0
7854 
4.0
6230 
1.0
3404 
5.0
3023 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014489
33.7%
2.07854
18.3%
4.06230
14.5%
1.03404
 
7.9%
5.03023
 
7.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.288700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.357493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014489
41.4%
2.07854
22.4%
4.06230
17.8%
1.03404
 
9.7%
5.03023
 
8.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
12693 
4.0
8724 
5.0
7537 
2.0
4378 
1.0
1668 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.012693
29.5%
4.08724
20.3%
5.07537
17.5%
2.04378
 
10.2%
1.01668
 
3.9%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.440310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.521362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.012693
36.3%
4.08724
24.9%
5.07537
21.5%
2.04378
 
12.5%
1.01668
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_20
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14884 
2.0
8223 
4.0
6510 
1.0
3070 
5.0
2313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014884
34.6%
2.08223
19.1%
4.06510
15.2%
1.03070
 
7.1%
5.02313
 
5.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.612466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.681916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014884
42.5%
2.08223
23.5%
4.06510
18.6%
1.03070
 
8.8%
5.02313
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_25
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14628 
2.0
7799 
4.0
6218 
1.0
3767 
5.0
2588 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row5.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014628
34.0%
2.07799
18.2%
4.06218
14.5%
1.03767
 
8.8%
5.02588
 
6.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.765149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:01.856942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014628
41.8%
2.07799
22.3%
4.06218
17.8%
1.03767
 
10.8%
5.02588
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_30
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14503 
2.0
7231 
4.0
6718 
1.0
3407 
5.0
3141 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row5.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014503
33.8%
2.07231
16.8%
4.06718
15.6%
1.03407
 
7.9%
5.03141
 
7.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:01.948209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.018826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014503
41.4%
2.07231
20.7%
4.06718
19.2%
1.03407
 
9.7%
5.03141
 
9.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_35
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13882 
4.0
7379 
2.0
6960 
5.0
3538 
1.0
3241 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.013882
32.3%
4.07379
17.2%
2.06960
16.2%
5.03538
 
8.2%
1.03241
 
7.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.110794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.179645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013882
39.7%
4.07379
21.1%
2.06960
19.9%
5.03538
 
10.1%
1.03241
 
9.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_40
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14159 
4.0
7102 
2.0
7007 
5.0
3531 
1.0
3201 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014159
33.0%
4.07102
16.5%
2.07007
16.3%
5.03531
 
8.2%
1.03201
 
7.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.271142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.362176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014159
40.5%
4.07102
20.3%
2.07007
20.0%
5.03531
 
10.1%
1.03201
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_45
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13916 
2.0
7358 
4.0
6892 
1.0
3433 
5.0
3401 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.013916
32.4%
2.07358
17.1%
4.06892
16.0%
1.03433
 
8.0%
5.03401
 
7.9%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.445256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.516442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013916
39.8%
2.07358
21.0%
4.06892
19.7%
1.03433
 
9.8%
5.03401
 
9.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_50
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14071 
2.0
7990 
4.0
6306 
1.0
3739 
5.0
2894 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014071
32.8%
2.07990
18.6%
4.06306
14.7%
1.03739
 
8.7%
5.02894
 
6.7%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.607859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.679600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014071
40.2%
2.07990
22.8%
4.06306
18.0%
1.03739
 
10.7%
5.02894
 
8.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_55
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14107 
2.0
7969 
4.0
6269 
1.0
3768 
5.0
2887 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row1.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014107
32.8%
2.07969
18.5%
4.06269
14.6%
1.03768
 
8.8%
5.02887
 
6.7%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.771874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.843620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014107
40.3%
2.07969
22.8%
4.06269
17.9%
1.03768
 
10.8%
5.02887
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14356 
2.0
7548 
4.0
6654 
1.0
3249 
5.0
3193 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014356
33.4%
2.07548
17.6%
4.06654
15.5%
1.03249
 
7.6%
5.03193
 
7.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:02.924691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:02.996326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014356
41.0%
2.07548
21.6%
4.06654
19.0%
1.03249
 
9.3%
5.03193
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_65
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14212 
4.0
7797 
2.0
6398 
5.0
4264 
1.0
2329 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014212
33.1%
4.07797
18.1%
2.06398
14.9%
5.04264
 
9.9%
1.02329
 
5.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.085592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.148734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014212
40.6%
4.07797
22.3%
2.06398
18.3%
5.04264
 
12.2%
1.02329
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HALTER_66
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13717 
4.0
7799 
2.0
6293 
5.0
4387 
1.0
2804 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.013717
31.9%
4.07799
18.2%
2.06293
14.6%
5.04387
 
10.2%
1.02804
 
6.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.240174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.321487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013717
39.2%
4.07799
22.3%
2.06293
18.0%
5.04387
 
12.5%
1.02804
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_ASIEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14572 
2.0
7516 
4.0
6544 
1.0
3243 
5.0
3125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014572
33.9%
2.07516
17.5%
4.06544
15.2%
1.03243
 
7.5%
5.03125
 
7.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.412785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.484507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014572
41.6%
2.07516
21.5%
4.06544
18.7%
1.03243
 
9.3%
5.03125
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_AUDI_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15193 
2.0
7176 
4.0
7029 
1.0
2908 
5.0
2694 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015193
35.4%
2.07176
16.7%
4.07029
16.4%
1.02908
 
6.8%
5.02694
 
6.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.576629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.645708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015193
43.4%
2.07176
20.5%
4.07029
20.1%
1.02908
 
8.3%
5.02694
 
7.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_BMW_BENZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14211 
4.0
8214 
2.0
5873 
5.0
4499 
1.0
2203 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row3.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014211
33.1%
4.08214
19.1%
2.05873
13.7%
5.04499
 
10.5%
1.02203
 
5.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.729763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.801281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014211
40.6%
4.08214
23.5%
2.05873
16.8%
5.04499
 
12.9%
1.02203
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_EUROPA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15043 
4.0
7383 
2.0
6838 
5.0
3381 
1.0
2355 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015043
35.0%
4.07383
17.2%
2.06838
15.9%
5.03381
 
7.9%
1.02355
 
5.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:03.892937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:03.972107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015043
43.0%
4.07383
21.1%
2.06838
19.5%
5.03381
 
9.7%
1.02355
 
6.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_FORD_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14299 
2.0
7981 
4.0
6130 
1.0
3825 
5.0
2765 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014299
33.3%
2.07981
18.6%
4.06130
14.3%
1.03825
 
8.9%
5.02765
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:04.074562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:04.138028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014299
40.9%
2.07981
22.8%
4.06130
17.5%
1.03825
 
10.9%
5.02765
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HERST_SONST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15033 
2.0
7544 
4.0
6905 
5.0
2764 
1.0
2754 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015033
35.0%
2.07544
17.6%
4.06905
16.1%
5.02764
 
6.4%
1.02754
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:04.229606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:04.301466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015033
43.0%
2.07544
21.6%
4.06905
19.7%
5.02764
 
7.9%
1.02754
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15205 
4.0
9052 
5.0
6581 
2.0
3547 
1.0
 
615

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015205
35.4%
4.09052
21.1%
5.06581
15.3%
2.03547
 
8.3%
1.0615
 
1.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:04.393615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:04.465038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015205
43.4%
4.09052
25.9%
5.06581
18.8%
2.03547
 
10.1%
1.0615
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_0_140
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.244
Minimum0
Maximum5
Zeros4632
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:04.536386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.523703774
Coefficient of variation (CV)0.679012377
Kurtosis-1.133511669
Mean2.244
Median Absolute Deviation (MAD)2
Skewness0.1097384947
Sum78540
Variance2.321673191
MonotonicityNot monotonic
2021-12-29T16:00:04.628224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312379
28.8%
110947
25.5%
04632
 
10.8%
43815
 
8.9%
52914
 
6.8%
2313
 
0.7%
(Missing)7962
18.5%
ValueCountFrequency (%)
04632
 
10.8%
110947
25.5%
2313
 
0.7%
312379
28.8%
43815
 
8.9%
52914
 
6.8%
ValueCountFrequency (%)
52914
 
6.8%
43815
 
8.9%
312379
28.8%
2313
 
0.7%
110947
25.5%
04632
 
10.8%

KBA13_KMH_110
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
27949 
3.0
3922 
2.0
3129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.027949
65.1%
3.03922
 
9.1%
2.03129
 
7.3%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:04.719552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:04.781044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.027949
79.9%
3.03922
 
11.2%
2.03129
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
11574 
4.0
8469 
3.0
7531 
2.0
4388 
5.0
3038 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row1.0
4th row5.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.011574
26.9%
4.08469
19.7%
3.07531
17.5%
2.04388
 
10.2%
5.03038
 
7.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:04.861061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:04.924586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.011574
33.1%
4.08469
24.2%
3.07531
21.5%
2.04388
 
12.5%
5.03038
 
8.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_140_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15796 
2.0
8033 
4.0
5912 
1.0
3461 
5.0
1798 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row4.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015796
36.8%
2.08033
18.7%
4.05912
 
13.8%
1.03461
 
8.1%
5.01798
 
4.2%
(Missing)7962
18.5%

Length

2021-12-29T16:00:05.025003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:05.087994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015796
45.1%
2.08033
23.0%
4.05912
 
16.9%
1.03461
 
9.9%
5.01798
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_180
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15732 
2.0
8080 
4.0
6289 
1.0
3168 
5.0
1731 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.015732
36.6%
2.08080
18.8%
4.06289
 
14.6%
1.03168
 
7.4%
5.01731
 
4.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:05.179640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:05.250581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015732
44.9%
2.08080
23.1%
4.06289
 
18.0%
1.03168
 
9.1%
5.01731
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_210
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15936 
4.0
7881 
2.0
6562 
5.0
2898 
1.0
1723 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row5.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015936
37.1%
4.07881
18.3%
2.06562
15.3%
5.02898
 
6.7%
1.01723
 
4.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:05.342305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:05.411901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015936
45.5%
4.07881
22.5%
2.06562
18.7%
5.02898
 
8.3%
1.01723
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KMH_211
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.496114286
Minimum0
Maximum5
Zeros5978
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:05.483217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.501063668
Coefficient of variation (CV)0.6013601527
Kurtosis-0.7015445236
Mean2.496114286
Median Absolute Deviation (MAD)1
Skewness-0.2377341883
Sum87364
Variance2.253192135
MonotonicityNot monotonic
2021-12-29T16:00:05.557263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312409
28.9%
26816
15.9%
05978
13.9%
44020
 
9.4%
53662
 
8.5%
12115
 
4.9%
(Missing)7962
18.5%
ValueCountFrequency (%)
05978
13.9%
12115
 
4.9%
26816
15.9%
312409
28.9%
44020
 
9.4%
53662
 
8.5%
ValueCountFrequency (%)
53662
 
8.5%
44020
 
9.4%
312409
28.9%
26816
15.9%
12115
 
4.9%
05978
13.9%

KBA13_KMH_250
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.4954
Minimum0
Maximum5
Zeros5985
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:05.647527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.500966472
Coefficient of variation (CV)0.6014933367
Kurtosis-0.7006080031
Mean2.4954
Median Absolute Deviation (MAD)1
Skewness-0.2373950975
Sum87339
Variance2.252900351
MonotonicityNot monotonic
2021-12-29T16:00:05.728615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312417
28.9%
26827
15.9%
05985
13.9%
44005
 
9.3%
53662
 
8.5%
12104
 
4.9%
(Missing)7962
18.5%
ValueCountFrequency (%)
05985
13.9%
12104
 
4.9%
26827
15.9%
312417
28.9%
44005
 
9.3%
53662
 
8.5%
ValueCountFrequency (%)
53662
 
8.5%
44005
 
9.3%
312417
28.9%
26827
15.9%
12104
 
4.9%
05985
13.9%

KBA13_KMH_251
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
29950 
3.0
4595 
2.0
 
455

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.029950
69.7%
3.04595
 
10.7%
2.0455
 
1.1%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:05.819736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:05.890614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.029950
85.6%
3.04595
 
13.1%
2.0455
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSAQUOT
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.007028571
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:05.951374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.04568999
Coefficient of variation (CV)0.3477486047
Kurtosis-0.3547459633
Mean3.007028571
Median Absolute Deviation (MAD)1
Skewness0.003674275742
Sum105246
Variance1.093467555
MonotonicityNot monotonic
2021-12-29T16:00:06.032391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314796
34.4%
27099
16.5%
47089
16.5%
53073
 
7.2%
12939
 
6.8%
04
 
< 0.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
04
 
< 0.1%
12939
 
6.8%
27099
16.5%
314796
34.4%
47089
16.5%
53073
 
7.2%
ValueCountFrequency (%)
53073
 
7.2%
47089
16.5%
314796
34.4%
27099
16.5%
12939
 
6.8%
04
 
< 0.1%

KBA13_KRSHERST_AUDI_VW
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.015542857
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:06.123385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.018634693
Coefficient of variation (CV)0.3377947988
Kurtosis-0.324204703
Mean3.015542857
Median Absolute Deviation (MAD)1
Skewness-0.03188613928
Sum105544
Variance1.037616637
MonotonicityNot monotonic
2021-12-29T16:00:06.204400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314755
34.3%
47713
18.0%
27179
16.7%
52680
 
6.2%
12669
 
6.2%
04
 
< 0.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
04
 
< 0.1%
12669
 
6.2%
27179
16.7%
314755
34.3%
47713
18.0%
52680
 
6.2%
ValueCountFrequency (%)
52680
 
6.2%
47713
18.0%
314755
34.3%
27179
16.7%
12669
 
6.2%
04
 
< 0.1%

KBA13_KRSHERST_BMW_BENZ
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean3.124314286
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:06.285303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.03121526
Coefficient of variation (CV)0.3300613081
Kurtosis-0.3311796463
Mean3.124314286
Median Absolute Deviation (MAD)1
Skewness0.01494440526
Sum109351
Variance1.063404913
MonotonicityNot monotonic
2021-12-29T16:00:06.366418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
315102
35.2%
47446
17.3%
26505
15.1%
53827
 
8.9%
12116
 
4.9%
04
 
< 0.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
04
 
< 0.1%
12116
 
4.9%
26505
15.1%
315102
35.2%
47446
17.3%
53827
 
8.9%
ValueCountFrequency (%)
53827
 
8.9%
47446
17.3%
315102
35.2%
26505
15.1%
12116
 
4.9%
04
 
< 0.1%

KBA13_KRSHERST_FORD_OPEL
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.972114286
Minimum0
Maximum5
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:06.457602image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.05621831
Coefficient of variation (CV)0.3553760752
Kurtosis-0.3987383048
Mean2.972114286
Median Absolute Deviation (MAD)1
Skewness-0.01122440687
Sum104024
Variance1.115597118
MonotonicityNot monotonic
2021-12-29T16:00:06.538809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
314503
33.8%
27195
16.7%
47117
16.6%
13312
 
7.7%
52869
 
6.7%
04
 
< 0.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
04
 
< 0.1%
13312
 
7.7%
27195
16.7%
314503
33.8%
47117
16.6%
52869
 
6.7%
ValueCountFrequency (%)
52869
 
6.7%
47117
16.6%
314503
33.8%
27195
16.7%
13312
 
7.7%
04
 
< 0.1%

KBA13_KRSSEG_KLEIN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
2.0
32078 
1.0
 
1664
3.0
 
1252
0.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.032078
74.7%
1.01664
 
3.9%
3.01252
 
2.9%
0.06
 
< 0.1%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:06.629988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:06.700916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.032078
91.7%
1.01664
 
4.8%
3.01252
 
3.6%
0.06
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_OBER
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
2.0
22912 
1.0
6605 
3.0
5468 
0.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.022912
53.3%
1.06605
 
15.4%
3.05468
 
12.7%
0.015
 
< 0.1%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:06.782027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:06.853536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.022912
65.5%
1.06605
 
18.9%
3.05468
 
15.6%
0.015
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSSEG_VAN
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
2.0
22127 
1.0
7065 
3.0
5781 
0.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.022127
51.5%
1.07065
 
16.4%
3.05781
 
13.5%
0.027
 
0.1%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:06.935407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:07.006731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.022127
63.2%
1.07065
 
20.2%
3.05781
 
16.5%
0.027
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KRSZUL_NEU
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
2.0
17080 
1.0
9113 
3.0
7798 
0.0
 
1009

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.017080
39.8%
1.09113
21.2%
3.07798
18.2%
0.01009
 
2.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:07.096049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:07.158835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.017080
48.8%
1.09113
26.0%
3.07798
22.3%
0.01009
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_0_60
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15869 
2.0
8043 
4.0
6360 
1.0
2871 
5.0
1857 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.015869
36.9%
2.08043
18.7%
4.06360
14.8%
1.02871
 
6.7%
5.01857
 
4.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:07.765988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:07.829577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015869
45.3%
2.08043
23.0%
4.06360
18.2%
1.02871
 
8.2%
5.01857
 
5.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_110
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.470428571
Minimum0
Maximum5
Zeros5425
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:07.911402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.445124631
Coefficient of variation (CV)0.5849692023
Kurtosis-0.620242832
Mean2.470428571
Median Absolute Deviation (MAD)1
Skewness-0.2343454077
Sum86465
Variance2.088385199
MonotonicityNot monotonic
2021-12-29T16:00:07.990401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312467
29.0%
27299
17.0%
05425
12.6%
44151
 
9.7%
53051
 
7.1%
12607
 
6.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
05425
12.6%
12607
 
6.1%
27299
17.0%
312467
29.0%
44151
 
9.7%
53051
 
7.1%
ValueCountFrequency (%)
53051
 
7.1%
44151
 
9.7%
312467
29.0%
27299
17.0%
12607
 
6.1%
05425
12.6%

KBA13_KW_120
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.390485714
Minimum0
Maximum5
Zeros3940
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:08.064050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.522484339
Coefficient of variation (CV)0.6368933017
Kurtosis-1.105172237
Mean2.390485714
Median Absolute Deviation (MAD)1
Skewness0.0152637046
Sum83667
Variance2.317958563
MonotonicityNot monotonic
2021-12-29T16:00:08.145475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312282
28.6%
19856
22.9%
44399
 
10.2%
03940
 
9.2%
53441
 
8.0%
21082
 
2.5%
(Missing)7962
18.5%
ValueCountFrequency (%)
03940
 
9.2%
19856
22.9%
21082
 
2.5%
312282
28.6%
44399
 
10.2%
53441
 
8.0%
ValueCountFrequency (%)
53441
 
8.0%
44399
 
10.2%
312282
28.6%
21082
 
2.5%
19856
22.9%
03940
 
9.2%

KBA13_KW_121
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.540857143
Minimum0
Maximum5
Zeros4018
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:08.226695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.443504248
Coefficient of variation (CV)0.5681170435
Kurtosis-0.6973264975
Mean2.540857143
Median Absolute Deviation (MAD)1
Skewness-0.1477194459
Sum88930
Variance2.083704514
MonotonicityNot monotonic
2021-12-29T16:00:08.318267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312500
29.1%
25824
13.6%
14851
 
11.3%
44104
 
9.6%
04018
 
9.4%
53703
 
8.6%
(Missing)7962
18.5%
ValueCountFrequency (%)
04018
 
9.4%
14851
 
11.3%
25824
13.6%
312500
29.1%
44104
 
9.6%
53703
 
8.6%
ValueCountFrequency (%)
53703
 
8.6%
44104
 
9.6%
312500
29.1%
25824
13.6%
14851
 
11.3%
04018
 
9.4%

KBA13_KW_30
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
1.0
25064 
2.0
6396 
3.0
3540 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.025064
58.3%
2.06396
 
14.9%
3.03540
 
8.2%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:08.419535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:08.501261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.025064
71.6%
2.06396
 
18.3%
3.03540
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_40
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.3016
Minimum0
Maximum5
Zeros4534
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:08.562571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.391753749
Coefficient of variation (CV)0.6046896718
Kurtosis-0.7141680072
Mean2.3016
Median Absolute Deviation (MAD)1
Skewness-0.02682376715
Sum80556
Variance1.936978497
MonotonicityNot monotonic
2021-12-29T16:00:08.644677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312064
28.1%
26474
15.1%
16212
14.5%
04534
 
10.6%
43376
 
7.9%
52340
 
5.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
04534
 
10.6%
16212
14.5%
26474
15.1%
312064
28.1%
43376
 
7.9%
52340
 
5.4%
ValueCountFrequency (%)
52340
 
5.4%
43376
 
7.9%
312064
28.1%
26474
15.1%
16212
14.5%
04534
 
10.6%

KBA13_KW_50
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.273028571
Minimum0
Maximum5
Zeros6649
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:08.734471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.43539939
Coefficient of variation (CV)0.631492014
Kurtosis-0.6861838019
Mean2.273028571
Median Absolute Deviation (MAD)1
Skewness-0.1459235187
Sum79556
Variance2.06037141
MonotonicityNot monotonic
2021-12-29T16:00:08.818584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311777
27.4%
28537
19.9%
06649
15.5%
43306
 
7.7%
12432
 
5.7%
52299
 
5.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
06649
15.5%
12432
 
5.7%
28537
19.9%
311777
27.4%
43306
 
7.7%
52299
 
5.4%
ValueCountFrequency (%)
52299
 
5.4%
43306
 
7.7%
311777
27.4%
28537
19.9%
12432
 
5.7%
06649
15.5%

KBA13_KW_60
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.257971429
Minimum0
Maximum5
Zeros6069
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:08.900657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.419072028
Coefficient of variation (CV)0.6284720923
Kurtosis-0.7035909971
Mean2.257971429
Median Absolute Deviation (MAD)1
Skewness-0.09672195701
Sum79029
Variance2.013765421
MonotonicityNot monotonic
2021-12-29T16:00:08.990381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311639
27.1%
28015
18.7%
06069
14.1%
13743
 
8.7%
43331
 
7.8%
52203
 
5.1%
(Missing)7962
18.5%
ValueCountFrequency (%)
06069
14.1%
13743
 
8.7%
28015
18.7%
311639
27.1%
43331
 
7.8%
52203
 
5.1%
ValueCountFrequency (%)
52203
 
5.1%
43331
 
7.8%
311639
27.1%
28015
18.7%
13743
 
8.7%
06069
14.1%

KBA13_KW_61_120
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16094 
4.0
7732 
2.0
6651 
5.0
2632 
1.0
1891 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row2.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.016094
37.5%
4.07732
18.0%
2.06651
15.5%
5.02632
 
6.1%
1.01891
 
4.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:09.084353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:09.155654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016094
46.0%
4.07732
22.1%
2.06651
19.0%
5.02632
 
7.5%
1.01891
 
5.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_KW_70
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.330857143
Minimum0
Maximum5
Zeros6422
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:09.227162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.444366005
Coefficient of variation (CV)0.6196716129
Kurtosis-0.6758502079
Mean2.330857143
Median Absolute Deviation (MAD)1
Skewness-0.1830933081
Sum81580
Variance2.086193157
MonotonicityNot monotonic
2021-12-29T16:00:09.308258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312135
28.2%
28088
18.8%
06422
14.9%
43540
 
8.2%
52506
 
5.8%
12309
 
5.4%
(Missing)7962
18.5%
ValueCountFrequency (%)
06422
14.9%
12309
 
5.4%
28088
18.8%
312135
28.2%
43540
 
8.2%
52506
 
5.8%
ValueCountFrequency (%)
52506
 
5.8%
43540
 
8.2%
312135
28.2%
28088
18.8%
12309
 
5.4%
06422
14.9%

KBA13_KW_80
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.3382
Minimum0
Maximum5
Zeros5746
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:09.389328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.424437289
Coefficient of variation (CV)0.6092025013
Kurtosis-0.6604049256
Mean2.3382
Median Absolute Deviation (MAD)1
Skewness-0.1471241492
Sum81837
Variance2.029021589
MonotonicityNot monotonic
2021-12-29T16:00:09.470767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312064
28.1%
27826
18.2%
05746
13.4%
43551
 
8.3%
13319
 
7.7%
52494
 
5.8%
(Missing)7962
18.5%
ValueCountFrequency (%)
05746
13.4%
13319
 
7.7%
27826
18.2%
312064
28.1%
43551
 
8.3%
52494
 
5.8%
ValueCountFrequency (%)
52494
 
5.8%
43551
 
8.3%
312064
28.1%
27826
18.2%
13319
 
7.7%
05746
13.4%

KBA13_KW_90
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.416828571
Minimum0
Maximum5
Zeros5936
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:09.558000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.446730116
Coefficient of variation (CV)0.5986068408
Kurtosis-0.6278961874
Mean2.416828571
Median Absolute Deviation (MAD)1
Skewness-0.227839395
Sum84589
Variance2.093028029
MonotonicityNot monotonic
2021-12-29T16:00:09.643656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312574
29.3%
27611
17.7%
05936
13.8%
43798
 
8.8%
52843
 
6.6%
12238
 
5.2%
(Missing)7962
18.5%
ValueCountFrequency (%)
05936
13.8%
12238
 
5.2%
27611
17.7%
312574
29.3%
43798
 
8.8%
52843
 
6.6%
ValueCountFrequency (%)
52843
 
6.6%
43798
 
8.8%
312574
29.3%
27611
17.7%
12238
 
5.2%
05936
13.8%

KBA13_MAZDA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15234 
2.0
7427 
4.0
7273 
5.0
2937 
1.0
2129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015234
35.5%
2.07427
17.3%
4.07273
16.9%
5.02937
 
6.8%
1.02129
 
5.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:09.735527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:09.796295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015234
43.5%
2.07427
21.2%
4.07273
20.8%
5.02937
 
8.4%
1.02129
 
6.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MERCEDES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14266 
4.0
8001 
2.0
6127 
5.0
4290 
1.0
2316 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014266
33.2%
4.08001
18.6%
2.06127
14.3%
5.04290
 
10.0%
1.02316
 
5.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:09.888176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:09.959422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014266
40.8%
4.08001
22.9%
2.06127
17.5%
5.04290
 
12.3%
1.02316
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_MOTOR
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
20750 
2.0
6724 
4.0
4441 
1.0
3085 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.020750
48.3%
2.06724
 
15.7%
4.04441
 
10.3%
1.03085
 
7.2%
(Missing)7962
 
18.5%

Length

2021-12-29T16:00:10.050762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.112215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.020750
59.3%
2.06724
 
19.2%
4.04441
 
12.7%
1.03085
 
8.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_NISSAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14682 
2.0
7935 
4.0
6829 
5.0
2950 
1.0
2604 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014682
34.2%
2.07935
18.5%
4.06829
15.9%
5.02950
 
6.9%
1.02604
 
6.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:10.194076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.265480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014682
41.9%
2.07935
22.7%
4.06829
19.5%
5.02950
 
8.4%
1.02604
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_OPEL
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14476 
2.0
7911 
4.0
6311 
1.0
3538 
5.0
2764 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014476
33.7%
2.07911
18.4%
4.06311
14.7%
1.03538
 
8.2%
5.02764
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:10.357101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.428828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014476
41.4%
2.07911
22.6%
4.06311
18.0%
1.03538
 
10.1%
5.02764
 
7.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_PEUGEOT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15187 
4.0
7738 
2.0
6561 
5.0
3225 
1.0
2289 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row5.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015187
35.3%
4.07738
18.0%
2.06561
15.3%
5.03225
 
7.5%
1.02289
 
5.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:10.518543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.602582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015187
43.4%
4.07738
22.1%
2.06561
18.7%
5.03225
 
9.2%
1.02289
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_RENAULT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14482 
2.0
7283 
4.0
7167 
5.0
3328 
1.0
2740 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014482
33.7%
2.07283
17.0%
4.07167
16.7%
5.03328
 
7.7%
1.02740
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:10.684380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.755409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014482
41.4%
2.07283
20.8%
4.07167
20.5%
5.03328
 
9.5%
1.02740
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GELAENDEWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15410 
2.0
7261 
4.0
6976 
1.0
2699 
5.0
2654 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015410
35.9%
2.07261
16.9%
4.06976
16.2%
1.02699
 
6.3%
5.02654
 
6.2%
(Missing)7962
18.5%

Length

2021-12-29T16:00:10.846687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:10.907426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015410
44.0%
2.07261
20.7%
4.06976
19.9%
1.02699
 
7.7%
5.02654
 
7.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_GROSSRAUMVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14736 
4.0
7667 
2.0
6706 
5.0
3608 
1.0
2283 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014736
34.3%
4.07667
17.8%
2.06706
15.6%
5.03608
 
8.4%
1.02283
 
5.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.008492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.079860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014736
42.1%
4.07667
21.9%
2.06706
19.2%
5.03608
 
10.3%
1.02283
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINST
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15018 
2.0
7594 
4.0
6603 
1.0
3163 
5.0
2622 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015018
35.0%
2.07594
17.7%
4.06603
15.4%
1.03163
 
7.4%
5.02622
 
6.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.169121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.232405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015018
42.9%
2.07594
21.7%
4.06603
18.9%
1.03163
 
9.0%
5.02622
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KLEINWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15005 
2.0
7659 
4.0
6494 
1.0
3258 
5.0
2584 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row3.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015005
34.9%
2.07659
17.8%
4.06494
15.1%
1.03258
 
7.6%
5.02584
 
6.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.324162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.393496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015005
42.9%
2.07659
21.9%
4.06494
18.6%
1.03258
 
9.3%
5.02584
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_KOMPAKTKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15238 
2.0
7557 
4.0
6427 
5.0
2901 
1.0
2877 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row4.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015238
35.5%
2.07557
17.6%
4.06427
15.0%
5.02901
 
6.8%
1.02877
 
6.7%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.484516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.558322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015238
43.5%
2.07557
21.6%
4.06427
18.4%
5.02901
 
8.3%
1.02877
 
8.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIVANS
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15442 
4.0
7180 
2.0
7107 
5.0
2894 
1.0
2377 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015442
35.9%
4.07180
16.7%
2.07107
16.5%
5.02894
 
6.7%
1.02377
 
5.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.648677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.719654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015442
44.1%
4.07180
20.5%
2.07107
20.3%
5.02894
 
8.3%
1.02377
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MINIWAGEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15050 
4.0
7390 
2.0
7174 
5.0
3097 
1.0
2289 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.015050
35.0%
4.07390
17.2%
2.07174
16.7%
5.03097
 
7.2%
1.02289
 
5.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.821755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:11.893158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015050
43.0%
4.07390
21.1%
2.07174
20.5%
5.03097
 
8.8%
1.02289
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_MITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15054 
4.0
7358 
2.0
6985 
5.0
3290 
1.0
2313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015054
35.0%
4.07358
17.1%
2.06985
16.3%
5.03290
 
7.7%
1.02313
 
5.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:11.985486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:12.055983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015054
43.0%
4.07358
21.0%
2.06985
20.0%
5.03290
 
9.4%
1.02313
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBEREMITTELKLASSE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14793 
4.0
7746 
2.0
6476 
5.0
3676 
1.0
2309 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row2.0
4th row5.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.014793
34.4%
4.07746
18.0%
2.06476
15.1%
5.03676
 
8.6%
1.02309
 
5.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:12.147138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:12.218538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014793
42.3%
4.07746
22.1%
2.06476
18.5%
5.03676
 
10.5%
1.02309
 
6.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_OBERKLASSE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.477914286
Minimum0
Maximum5
Zeros3823
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:12.310212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.480589843
Coefficient of variation (CV)0.5975145514
Kurtosis-0.8957732887
Mean2.477914286
Median Absolute Deviation (MAD)1
Skewness-0.04962140726
Sum86727
Variance2.192146283
MonotonicityNot monotonic
2021-12-29T16:00:12.381311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312375
28.8%
17239
16.8%
43950
 
9.2%
23834
 
8.9%
03823
 
8.9%
53779
 
8.8%
(Missing)7962
18.5%
ValueCountFrequency (%)
03823
 
8.9%
17239
16.8%
23834
 
8.9%
312375
28.8%
43950
 
9.2%
53779
 
8.8%
ValueCountFrequency (%)
53779
 
8.8%
43950
 
9.2%
312375
28.8%
23834
 
8.9%
17239
16.8%
03823
 
8.9%

KBA13_SEG_SONSTIGE
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16028 
2.0
7526 
4.0
7197 
5.0
2592 
1.0
1657 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row5.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.016028
37.3%
2.07526
17.5%
4.07197
16.8%
5.02592
 
6.0%
1.01657
 
3.9%
(Missing)7962
18.5%

Length

2021-12-29T16:00:12.483479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:12.544933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016028
45.8%
2.07526
21.5%
4.07197
20.6%
5.02592
 
7.4%
1.01657
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_SPORTWAGEN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.574571429
Minimum0
Maximum5
Zeros3526
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:12.646597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.44336365
Coefficient of variation (CV)0.5606228802
Kurtosis-0.7164490729
Mean2.574571429
Median Absolute Deviation (MAD)1
Skewness-0.09286666306
Sum90110
Variance2.083298625
MonotonicityNot monotonic
2021-12-29T16:00:12.717840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311632
27.1%
26463
15.0%
15113
11.9%
44155
 
9.7%
54111
 
9.6%
03526
 
8.2%
(Missing)7962
18.5%
ValueCountFrequency (%)
03526
 
8.2%
15113
11.9%
26463
15.0%
311632
27.1%
44155
 
9.7%
54111
 
9.6%
ValueCountFrequency (%)
54111
 
9.6%
44155
 
9.7%
311632
27.1%
26463
15.0%
15113
11.9%
03526
 
8.2%

KBA13_SEG_UTILITIES
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15406 
2.0
7300 
4.0
7109 
5.0
2744 
1.0
2441 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row2.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.015406
35.9%
2.07300
17.0%
4.07109
16.5%
5.02744
 
6.4%
1.02441
 
5.7%
(Missing)7962
18.5%

Length

2021-12-29T16:00:12.829662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:12.900693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015406
44.0%
2.07300
20.9%
4.07109
20.3%
5.02744
 
7.8%
1.02441
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_VAN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15281 
4.0
7453 
2.0
6808 
5.0
3206 
1.0
2252 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015281
35.6%
4.07453
17.3%
2.06808
15.8%
5.03206
 
7.5%
1.02252
 
5.2%
(Missing)7962
18.5%

Length

2021-12-29T16:00:12.992305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:13.063041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015281
43.7%
4.07453
21.3%
2.06808
19.5%
5.03206
 
9.2%
1.02252
 
6.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SEG_WOHNMOBILE
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.486457143
Minimum0
Maximum5
Zeros3870
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:13.144362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.406348448
Coefficient of variation (CV)0.5656033333
Kurtosis-0.616022627
Mean2.486457143
Median Absolute Deviation (MAD)1
Skewness-0.07861841807
Sum87026
Variance1.977815957
MonotonicityNot monotonic
2021-12-29T16:00:13.235811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311748
27.3%
27469
17.4%
14722
11.0%
03870
 
9.0%
43833
 
8.9%
53358
 
7.8%
(Missing)7962
18.5%
ValueCountFrequency (%)
03870
 
9.0%
14722
11.0%
27469
17.4%
311748
27.3%
43833
 
8.9%
53358
 
7.8%
ValueCountFrequency (%)
53358
 
7.8%
43833
 
8.9%
311748
27.3%
27469
17.4%
14722
11.0%
03870
 
9.0%

KBA13_SITZE_4
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13828 
4.0
7907 
2.0
6459 
5.0
4257 
1.0
2549 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row5.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.013828
32.2%
4.07907
18.4%
2.06459
15.0%
5.04257
 
9.9%
1.02549
 
5.9%
(Missing)7962
18.5%

Length

2021-12-29T16:00:13.327479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:13.409235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013828
39.5%
4.07907
22.6%
2.06459
18.5%
5.04257
 
12.2%
1.02549
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_5
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
13322 
2.0
8264 
4.0
6221 
1.0
4459 
5.0
2734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row4.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.013322
31.0%
2.08264
19.2%
4.06221
14.5%
1.04459
 
10.4%
5.02734
 
6.4%
(Missing)7962
18.5%

Length

2021-12-29T16:00:13.501103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:13.582479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013322
38.1%
2.08264
23.6%
4.06221
17.8%
1.04459
 
12.7%
5.02734
 
7.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_SITZE_6
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14129 
4.0
7861 
2.0
6375 
5.0
3847 
1.0
2788 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014129
32.9%
4.07861
18.3%
2.06375
14.8%
5.03847
 
9.0%
1.02788
 
6.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:13.664658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:13.736710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014129
40.4%
4.07861
22.5%
2.06375
18.2%
5.03847
 
11.0%
1.02788
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_TOYOTA
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
14808 
4.0
7379 
2.0
7035 
5.0
3506 
1.0
2272 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row4.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.014808
34.5%
4.07379
17.2%
2.07035
16.4%
5.03506
 
8.2%
1.02272
 
5.3%
(Missing)7962
18.5%

Length

2021-12-29T16:00:13.827821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:13.896947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014808
42.3%
4.07379
21.1%
2.07035
20.1%
5.03506
 
10.0%
1.02272
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_0
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15461 
4.0
8620 
2.0
5644 
5.0
3963 
1.0
 
1312

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
3.015461
36.0%
4.08620
20.1%
2.05644
 
13.1%
5.03963
 
9.2%
1.01312
 
3.1%
(Missing)7962
18.5%

Length

2021-12-29T16:00:13.988620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:14.051423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015461
44.2%
4.08620
24.6%
2.05644
 
16.1%
5.03963
 
11.3%
1.01312
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16083 
4.0
7541 
2.0
6955 
5.0
2479 
1.0
1942 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.016083
37.4%
4.07541
17.6%
2.06955
16.2%
5.02479
 
5.8%
1.01942
 
4.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:14.142644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:14.213500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016083
46.0%
4.07541
21.5%
2.06955
19.9%
5.02479
 
7.1%
1.01942
 
5.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_1_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15747 
2.0
7619 
4.0
6790 
1.0
2865 
5.0
1979 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row3.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.015747
36.7%
2.07619
17.7%
4.06790
15.8%
1.02865
 
6.7%
5.01979
 
4.6%
(Missing)7962
18.5%

Length

2021-12-29T16:00:14.305096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:14.375956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015747
45.0%
2.07619
21.8%
4.06790
19.4%
1.02865
 
8.2%
5.01979
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
16004 
2.0
8104 
4.0
6675 
1.0
2265 
5.0
1952 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row3.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.016004
37.3%
2.08104
18.9%
4.06675
15.5%
1.02265
 
5.3%
5.01952
 
4.5%
(Missing)7962
18.5%

Length

2021-12-29T16:00:14.477878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:14.538955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.016004
45.7%
2.08104
23.2%
4.06675
19.1%
1.02265
 
6.5%
5.01952
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KBA13_VORB_3
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.152171429
Minimum0
Maximum5
Zeros7228
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:14.630164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.424655995
Coefficient of variation (CV)0.6619621355
Kurtosis-0.737749626
Mean2.152171429
Median Absolute Deviation (MAD)1
Skewness-0.07006311434
Sum75326
Variance2.029644703
MonotonicityNot monotonic
2021-12-29T16:00:14.701646image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
311138
25.9%
28902
20.7%
07228
16.8%
12914
 
6.8%
42896
 
6.7%
51922
 
4.5%
(Missing)7962
18.5%
ValueCountFrequency (%)
07228
16.8%
12914
 
6.8%
28902
20.7%
311138
25.9%
42896
 
6.7%
51922
 
4.5%
ValueCountFrequency (%)
51922
 
4.5%
42896
 
6.7%
311138
25.9%
28902
20.7%
12914
 
6.8%
07228
16.8%

KBA13_VW
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7962
Missing (%)18.5%
Memory size335.8 KiB
3.0
15462 
2.0
7250 
4.0
6747 
1.0
2981 
5.0
2560 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.015462
36.0%
2.07250
16.9%
4.06747
15.7%
1.02981
 
6.9%
5.02560
 
6.0%
(Missing)7962
18.5%

Length

2021-12-29T16:00:14.801219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:14.865869image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.015462
44.2%
2.07250
20.7%
4.06747
19.3%
1.02981
 
8.5%
5.02560
 
7.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KK_KUNDENTYP
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing25316
Missing (%)58.9%
Infinite0
Infinite (%)0.0%
Mean3.41533492
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:14.946206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.650214621
Coefficient of variation (CV)0.483177978
Kurtosis-1.185048835
Mean3.41533492
Median Absolute Deviation (MAD)1
Skewness0.1064877933
Sum60267
Variance2.723208295
MonotonicityNot monotonic
2021-12-29T16:00:15.027824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
33618
 
8.4%
23280
 
7.6%
52832
 
6.6%
12697
 
6.3%
42659
 
6.2%
62560
 
6.0%
(Missing)25316
58.9%
ValueCountFrequency (%)
12697
6.3%
23280
7.6%
33618
8.4%
42659
6.2%
52832
6.6%
62560
6.0%
ValueCountFrequency (%)
62560
6.0%
52832
6.6%
42659
6.2%
33618
8.4%
23280
7.6%
12697
6.3%

KKK
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8445
Missing (%)19.7%
Memory size335.8 KiB
3.0
11890 
2.0
8690 
4.0
7350 
1.0
5102 
0.0
1485 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row1.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.011890
27.7%
2.08690
20.2%
4.07350
17.1%
1.05102
11.9%
0.01485
 
3.5%
(Missing)8445
19.7%

Length

2021-12-29T16:00:15.119369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:15.190943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.011890
34.4%
2.08690
25.2%
4.07350
21.3%
1.05102
14.8%
0.01485
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KOMBIALTER
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
4
27935 
9
7054 
3
5982 
2
 
1439
1
 
552

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
427935
65.0%
97054
 
16.4%
35982
 
13.9%
21439
 
3.3%
1552
 
1.3%

Length

2021-12-29T16:00:15.283361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:15.353181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
427935
65.0%
97054
 
16.4%
35982
 
13.9%
21439
 
3.3%
1552
 
1.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KONSUMNAEHE
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing6997
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean3.151258168
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:15.434733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.510438557
Coefficient of variation (CV)0.4793128575
Kurtosis-1.061247479
Mean3.151258168
Median Absolute Deviation (MAD)1
Skewness0.07749730677
Sum113335
Variance2.281424635
MonotonicityNot monotonic
2021-12-29T16:00:15.508726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
38088
18.8%
57390
17.2%
16912
16.1%
46206
14.4%
25997
14.0%
61213
 
2.8%
7159
 
0.4%
(Missing)6997
16.3%
ValueCountFrequency (%)
16912
16.1%
25997
14.0%
38088
18.8%
46206
14.4%
57390
17.2%
61213
 
2.8%
7159
 
0.4%
ValueCountFrequency (%)
7159
 
0.4%
61213
 
2.8%
57390
17.2%
46206
14.4%
38088
18.8%
25997
14.0%
16912
16.1%

KONSUMZELLE
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
0.0
28436 
1.0
6749 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.028436
66.2%
1.06749
 
15.7%
(Missing)7777
 
18.1%

Length

2021-12-29T16:00:15.610557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:15.671702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.028436
80.8%
1.06749
 
19.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LP_FAMILIE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean4.109403404
Minimum0
Maximum11
Zeros8208
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:15.740584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q310
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.392075301
Coefficient of variation (CV)1.068786602
Kurtosis-1.467511144
Mean4.109403404
Median Absolute Deviation (MAD)2
Skewness0.6328992055
Sum174062
Variance19.29032545
MonotonicityNot monotonic
2021-12-29T16:00:15.833875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
112228
28.5%
108577
20.0%
08208
19.1%
27093
16.5%
114303
 
10.0%
8600
 
1.4%
9590
 
1.4%
7377
 
0.9%
5171
 
0.4%
4119
 
0.3%
Other values (2)91
 
0.2%
(Missing)605
 
1.4%
ValueCountFrequency (%)
08208
19.1%
112228
28.5%
27093
16.5%
327
 
0.1%
4119
 
0.3%
5171
 
0.4%
664
 
0.1%
7377
 
0.9%
8600
 
1.4%
9590
 
1.4%
ValueCountFrequency (%)
114303
10.0%
108577
20.0%
9590
 
1.4%
8600
 
1.4%
7377
 
0.9%
664
 
0.1%
5171
 
0.4%
4119
 
0.3%
327
 
0.1%
27093
16.5%

LP_FAMILIE_GROB
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.33441934
Minimum0
Maximum5
Zeros8208
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:15.914880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.979889627
Coefficient of variation (CV)0.8481293796
Kurtosis-1.508513307
Mean2.33441934
Median Absolute Deviation (MAD)2
Skewness0.3841764798
Sum98879
Variance3.919962934
MonotonicityNot monotonic
2021-12-29T16:00:16.005801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
513470
31.4%
112228
28.5%
08208
19.1%
27093
16.5%
41041
 
2.4%
3317
 
0.7%
(Missing)605
 
1.4%
ValueCountFrequency (%)
08208
19.1%
112228
28.5%
27093
16.5%
3317
 
0.7%
41041
 
2.4%
513470
31.4%
ValueCountFrequency (%)
513470
31.4%
41041
 
2.4%
3317
 
0.7%
27093
16.5%
112228
28.5%
08208
19.1%

LP_LEBENSPHASE_FEIN
Real number (ℝ≥0)

MISSING
ZEROS

Distinct41
Distinct (%)0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean17.66107137
Minimum0
Maximum40
Zeros8298
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:16.114928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median15
Q332
95-th percentile40
Maximum40
Range40
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.08570151
Coefficient of variation (CV)0.797556457
Kurtosis-1.364828806
Mean17.66107137
Median Absolute Deviation (MAD)15
Skewness0.2793471299
Sum748070
Variance198.406987
MonotonicityNot monotonic
2021-12-29T16:00:16.248322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
08298
19.3%
63067
 
7.1%
402468
 
5.7%
82416
 
5.6%
382076
 
4.8%
132061
 
4.8%
121920
 
4.5%
201854
 
4.3%
311666
 
3.9%
321633
 
3.8%
Other values (31)14898
34.7%
ValueCountFrequency (%)
08298
19.3%
1131
 
0.3%
2124
 
0.3%
375
 
0.2%
478
 
0.2%
5690
 
1.6%
63067
 
7.1%
7392
 
0.9%
82416
 
5.6%
9653
 
1.5%
ValueCountFrequency (%)
402468
5.7%
391255
2.9%
382076
4.8%
371191
2.8%
361440
3.4%
35522
 
1.2%
34344
 
0.8%
33243
 
0.6%
321633
3.8%
311666
3.9%

LP_LEBENSPHASE_GROB
Real number (ℝ≥0)

MISSING
ZEROS

Distinct13
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean5.274995868
Minimum0
Maximum12
Zeros8273
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:16.369776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.470538304
Coefficient of variation (CV)0.8474960768
Kurtosis-1.36366669
Mean5.274995868
Median Absolute Deviation (MAD)4
Skewness0.4290829214
Sum223433
Variance19.98571272
MonotonicityNot monotonic
2021-12-29T16:00:16.462348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
128430
19.6%
08273
19.3%
26565
15.3%
35225
12.2%
54150
9.7%
103299
 
7.7%
42943
 
6.9%
111109
 
2.6%
8787
 
1.8%
9597
 
1.4%
Other values (3)979
 
2.3%
(Missing)605
 
1.4%
ValueCountFrequency (%)
08273
19.3%
1408
 
0.9%
26565
15.3%
35225
12.2%
42943
 
6.9%
54150
9.7%
6317
 
0.7%
7254
 
0.6%
8787
 
1.8%
9597
 
1.4%
ValueCountFrequency (%)
128430
19.6%
111109
 
2.6%
103299
 
7.7%
9597
 
1.4%
8787
 
1.8%
7254
 
0.6%
6317
 
0.7%
54150
9.7%
42943
 
6.9%
35225
12.2%

LP_STATUS_FEIN
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean5.92700144
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:16.556812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.39833627
Coefficient of variation (CV)0.5733651838
Kurtosis-1.491594681
Mean5.92700144
Median Absolute Deviation (MAD)4
Skewness-0.1591674838
Sum251050
Variance11.54868941
MonotonicityNot monotonic
2021-12-29T16:00:16.624338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
109131
21.3%
98479
19.7%
18087
18.8%
56114
14.2%
34199
9.8%
42537
 
5.9%
61898
 
4.4%
71243
 
2.9%
2569
 
1.3%
8100
 
0.2%
(Missing)605
 
1.4%
ValueCountFrequency (%)
18087
18.8%
2569
 
1.3%
34199
9.8%
42537
 
5.9%
56114
14.2%
61898
 
4.4%
71243
 
2.9%
8100
 
0.2%
98479
19.7%
109131
21.3%
ValueCountFrequency (%)
109131
21.3%
98479
19.7%
8100
 
0.2%
71243
 
2.9%
61898
 
4.4%
56114
14.2%
42537
 
5.9%
34199
9.8%
2569
 
1.3%
18087
18.8%

LP_STATUS_GROB
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
2.0
12850 
5.0
9131 
1.0
8656 
4.0
8579 
3.0
3141 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row5.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.012850
29.9%
5.09131
21.3%
1.08656
20.1%
4.08579
20.0%
3.03141
 
7.3%
(Missing)605
 
1.4%

Length

2021-12-29T16:00:16.716452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:16.785987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.012850
30.3%
5.09131
21.6%
1.08656
20.4%
4.08579
20.3%
3.03141
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MIN_GEBAEUDEJAHR
Real number (ℝ≥0)

MISSING

Distinct31
Distinct (%)0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean1992.855848
Minimum1985
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:16.869550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1992
Q11992
median1992
Q31992
95-th percentile1997
Maximum2015
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.456724464
Coefficient of variation (CV)0.001232765765
Kurtosis22.94704159
Mean1992.855848
Median Absolute Deviation (MAD)0
Skewness4.204989958
Sum70118633
Variance6.035495093
MonotonicityNot monotonic
2021-12-29T16:00:16.961452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
199225840
60.1%
19943745
 
8.7%
19931270
 
3.0%
1995941
 
2.2%
1996668
 
1.6%
1997578
 
1.3%
1991325
 
0.8%
1990281
 
0.7%
2000251
 
0.6%
2001159
 
0.4%
Other values (21)1127
 
2.6%
(Missing)7777
 
18.1%
ValueCountFrequency (%)
19855
 
< 0.1%
19868
 
< 0.1%
198731
 
0.1%
198849
 
0.1%
1989116
 
0.3%
1990281
 
0.7%
1991325
 
0.8%
199225840
60.1%
19931270
 
3.0%
19943745
 
8.7%
ValueCountFrequency (%)
20159
 
< 0.1%
201417
 
< 0.1%
201318
 
< 0.1%
201232
0.1%
201127
0.1%
201024
0.1%
200934
0.1%
200839
0.1%
200751
0.1%
200645
0.1%

MOBI_RASTER
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean2.691942589
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:17.063459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.600008222
Coefficient of variation (CV)0.5943693705
Kurtosis-1.089556315
Mean2.691942589
Median Absolute Deviation (MAD)2
Skewness0.4437766103
Sum94716
Variance2.560026311
MonotonicityNot monotonic
2021-12-29T16:00:17.135244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
112559
29.2%
36148
14.3%
45150
12.0%
25029
11.7%
54739
 
11.0%
61560
 
3.6%
(Missing)7777
18.1%
ValueCountFrequency (%)
112559
29.2%
25029
11.7%
36148
14.3%
45150
12.0%
54739
 
11.0%
61560
 
3.6%
ValueCountFrequency (%)
61560
 
3.6%
54739
 
11.0%
45150
12.0%
36148
14.3%
25029
11.7%
112559
29.2%

MOBI_REGIO
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing8648
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean3.328612228
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:17.227497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.414178205
Coefficient of variation (CV)0.4248551972
Kurtosis-1.181409417
Mean3.328612228
Median Absolute Deviation (MAD)1
Skewness-0.3423400394
Sum114218
Variance1.999899995
MonotonicityNot monotonic
2021-12-29T16:00:17.306429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
59351
21.8%
48230
19.2%
36366
14.8%
15381
12.5%
24963
11.6%
623
 
0.1%
(Missing)8648
20.1%
ValueCountFrequency (%)
15381
12.5%
24963
11.6%
36366
14.8%
48230
19.2%
59351
21.8%
623
 
0.1%
ValueCountFrequency (%)
623
 
0.1%
59351
21.8%
48230
19.2%
36366
14.8%
24963
11.6%
15381
12.5%

NATIONALITAET_KZ
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
1
34424 
0
7316 
2
 
720
3
 
502

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
134424
80.1%
07316
 
17.0%
2720
 
1.7%
3502
 
1.2%

Length

2021-12-29T16:00:17.399559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:17.460596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
134424
80.1%
07316
 
17.0%
2720
 
1.7%
3502
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ONLINE_AFFINITAET
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean2.57985693
Minimum0
Maximum5
Zeros2156
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:17.531483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.360915219
Coefficient of variation (CV)0.5275157715
Kurtosis-0.8402103647
Mean2.57985693
Median Absolute Deviation (MAD)1
Skewness0.1488308148
Sum109275
Variance1.852090234
MonotonicityNot monotonic
2021-12-29T16:00:17.620603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
214437
33.6%
48639
20.1%
16813
15.9%
36264
14.6%
54048
 
9.4%
02156
 
5.0%
(Missing)605
 
1.4%
ValueCountFrequency (%)
02156
 
5.0%
16813
15.9%
214437
33.6%
36264
14.6%
48639
20.1%
54048
 
9.4%
ValueCountFrequency (%)
54048
 
9.4%
48639
20.1%
36264
14.6%
214437
33.6%
16813
15.9%
02156
 
5.0%

ORTSGR_KLS9
Real number (ℝ≥0)

MISSING

Distinct10
Distinct (%)< 0.1%
Missing7951
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean5.152352118
Minimum0
Maximum9
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:17.713790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.387266
Coefficient of variation (CV)0.4633351808
Kurtosis-1.032735019
Mean5.152352118
Median Absolute Deviation (MAD)2
Skewness0.1326188369
Sum180389
Variance5.699038955
MonotonicityNot monotonic
2021-12-29T16:00:17.794770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
56229
14.5%
45336
12.4%
94626
10.8%
34315
10.0%
73625
8.4%
23497
8.1%
82879
 
6.7%
62684
 
6.2%
11816
 
4.2%
04
 
< 0.1%
(Missing)7951
18.5%
ValueCountFrequency (%)
04
 
< 0.1%
11816
 
4.2%
23497
8.1%
34315
10.0%
45336
12.4%
56229
14.5%
62684
6.2%
73625
8.4%
82879
6.7%
94626
10.8%
ValueCountFrequency (%)
94626
10.8%
82879
6.7%
73625
8.4%
62684
6.2%
56229
14.5%
45336
12.4%
34315
10.0%
23497
8.1%
11816
 
4.2%
04
 
< 0.1%

OST_WEST_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Memory size335.8 KiB
W
26752 
O
8433 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowW
3rd rowO
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W26752
62.3%
O8433
 
19.6%
(Missing)7777
 
18.1%

Length

2021-12-29T16:00:17.894525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:17.965587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
w26752
76.0%
o8433
 
24.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG1
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
2.0
11799 
3.0
11012 
1.0
6851 
4.0
4918 
0.0
 
229

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row4.0
4th row4.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.011799
27.5%
3.011012
25.6%
1.06851
15.9%
4.04918
11.4%
0.0229
 
0.5%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.036391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.099227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.011799
33.9%
3.011012
31.6%
1.06851
19.7%
4.04918
14.1%
0.0229
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG2
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
3.0
13811 
2.0
10389 
4.0
7305 
1.0
2935 
0.0
 
369

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row0.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.013811
32.1%
2.010389
24.2%
4.07305
17.0%
1.02935
 
6.8%
0.0369
 
0.9%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.190935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.262615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013811
39.7%
2.010389
29.8%
4.07305
21.0%
1.02935
 
8.4%
0.0369
 
1.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG3
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
1.0
11608 
2.0
10653 
0.0
6481 
3.0
6067 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.011608
27.0%
2.010653
24.8%
0.06481
15.1%
3.06067
14.1%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.355139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.424459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.011608
33.3%
2.010653
30.6%
0.06481
18.6%
3.06067
17.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_ANTG4
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
0.0
17981 
1.0
12259 
2.0
4569 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.017981
41.9%
1.012259
28.5%
2.04569
 
10.6%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.517361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.588682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.017981
51.7%
1.012259
35.2%
2.04569
 
13.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_BAUMAX
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
1.0
24379 
5.0
3479 
2.0
2847 
4.0
 
2103
3.0
 
2001

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row1.0
4th row1.0
5th row4.0

Common Values

ValueCountFrequency (%)
1.024379
56.7%
5.03479
 
8.1%
2.02847
 
6.6%
4.02103
 
4.9%
3.02001
 
4.7%
(Missing)8153
 
19.0%

Length

2021-12-29T16:00:18.669804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.740635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.024379
70.0%
5.03479
 
10.0%
2.02847
 
8.2%
4.02103
 
6.0%
3.02001
 
5.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_GBZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
3.0
13033 
4.0
8600 
5.0
6988 
2.0
4468 
1.0
1720 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row4.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.013033
30.3%
4.08600
20.0%
5.06988
16.3%
2.04468
 
10.4%
1.01720
 
4.0%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.831831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:18.892914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.013033
37.4%
4.08600
24.7%
5.06988
20.1%
2.04468
 
12.8%
1.01720
 
4.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PLZ8_HHZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8153
Missing (%)19.0%
Memory size335.8 KiB
3.0
14893 
4.0
9142 
5.0
6946 
2.0
3315 
1.0
 
513

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.014893
34.7%
4.09142
21.3%
5.06946
16.2%
2.03315
 
7.7%
1.0513
 
1.2%
(Missing)8153
19.0%

Length

2021-12-29T16:00:18.983947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:19.065838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.014893
42.8%
4.09142
26.3%
5.06946
20.0%
2.03315
 
9.5%
1.0513
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PRAEGENDE_JUGENDJAHRE
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.427750105
Minimum0
Maximum15
Zeros7454
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:19.147702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile12
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.753186573
Coefficient of variation (CV)0.8476509477
Kurtosis0.206027239
Mean4.427750105
Median Absolute Deviation (MAD)3
Skewness0.8627510551
Sum190225
Variance14.08640945
MonotonicityNot monotonic
2021-12-29T16:00:19.237866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
07454
17.4%
37002
16.3%
55828
13.6%
14315
10.0%
83926
9.1%
43567
8.3%
62185
 
5.1%
22081
 
4.8%
91876
 
4.4%
141100
 
2.6%
Other values (6)3628
8.4%
ValueCountFrequency (%)
07454
17.4%
14315
10.0%
22081
 
4.8%
37002
16.3%
43567
8.3%
55828
13.6%
62185
 
5.1%
7403
 
0.9%
83926
9.1%
91876
 
4.4%
ValueCountFrequency (%)
15650
 
1.5%
141100
 
2.6%
13162
 
0.4%
12313
 
0.7%
111099
 
2.6%
101001
 
2.3%
91876
4.4%
83926
9.1%
7403
 
0.9%
62185
5.1%

REGIOTYP
Real number (ℝ≥0)

MISSING
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing8445
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean4.126227656
Minimum0
Maximum7
Zeros1485
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:19.339536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q36
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.014987779
Coefficient of variation (CV)0.4883365501
Kurtosis-1.014061662
Mean4.126227656
Median Absolute Deviation (MAD)1
Skewness-0.3807023729
Sum142425
Variance4.060175748
MonotonicityNot monotonic
2021-12-29T16:00:19.412801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
68065
18.8%
56651
15.5%
24709
11.0%
34347
10.1%
73260
 
7.6%
43167
 
7.4%
12833
 
6.6%
01485
 
3.5%
(Missing)8445
19.7%
ValueCountFrequency (%)
01485
 
3.5%
12833
 
6.6%
24709
11.0%
34347
10.1%
43167
 
7.4%
56651
15.5%
68065
18.8%
73260
7.6%
ValueCountFrequency (%)
73260
7.6%
68065
18.8%
56651
15.5%
43167
 
7.4%
34347
10.1%
24709
11.0%
12833
 
6.6%
01485
 
3.5%

RELAT_AB
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing7951
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.958098883
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:19.511952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3472446
Coefficient of variation (CV)0.4554427194
Kurtosis-0.9665782959
Mean2.958098883
Median Absolute Deviation (MAD)1
Skewness0.05587733084
Sum103566
Variance1.815068011
MonotonicityNot monotonic
2021-12-29T16:00:19.594835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
312140
28.3%
17016
16.3%
56481
15.1%
24903
11.4%
44464
 
10.4%
97
 
< 0.1%
(Missing)7951
18.5%
ValueCountFrequency (%)
17016
16.3%
24903
11.4%
312140
28.3%
44464
 
10.4%
56481
15.1%
97
 
< 0.1%
ValueCountFrequency (%)
97
 
< 0.1%
56481
15.1%
44464
 
10.4%
312140
28.3%
24903
11.4%
17016
16.3%

RETOURTYP_BK_S
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
17158 
3.0
15412 
2.0
5127 
4.0
3664 
1.0
 
996

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row3.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.017158
39.9%
3.015412
35.9%
2.05127
 
11.9%
4.03664
 
8.5%
1.0996
 
2.3%
(Missing)605
 
1.4%

Length

2021-12-29T16:00:19.695977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:19.775009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.017158
40.5%
3.015412
36.4%
2.05127
 
12.1%
4.03664
 
8.7%
1.0996
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_KEIN_ANREIZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
1.0
12330 
4.0
10720 
2.0
10450 
3.0
7573 
5.0
1284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.012330
28.7%
4.010720
25.0%
2.010450
24.3%
3.07573
17.6%
5.01284
 
3.0%
(Missing)605
 
1.4%

Length

2021-12-29T16:00:19.866419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:19.937533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.012330
29.1%
4.010720
25.3%
2.010450
24.7%
3.07573
17.9%
5.01284
 
3.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_SCHNAEPPCHEN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing605
Missing (%)1.4%
Memory size335.8 KiB
5.0
24497 
4.0
7152 
3.0
4789 
2.0
4108 
1.0
 
1811

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row1.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.024497
57.0%
4.07152
 
16.6%
3.04789
 
11.1%
2.04108
 
9.6%
1.01811
 
4.2%
(Missing)605
 
1.4%

Length

2021-12-29T16:00:20.683782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:20.755080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.024497
57.8%
4.07152
 
16.9%
3.04789
 
11.3%
2.04108
 
9.7%
1.01811
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RT_UEBERGROESSE
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)< 0.1%
Missing6380
Missing (%)14.9%
Infinite0
Infinite (%)0.0%
Mean2.201902575
Minimum0
Maximum5
Zeros287
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:20.836668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.306469589
Coefficient of variation (CV)0.5933366914
Kurtosis-0.4136518855
Mean2.201902575
Median Absolute Deviation (MAD)1
Skewness0.8096038959
Sum80550
Variance1.706862786
MonotonicityNot monotonic
2021-12-29T16:00:20.928170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
114325
33.3%
29441
22.0%
36103
14.2%
53330
 
7.8%
43096
 
7.2%
0287
 
0.7%
(Missing)6380
14.9%
ValueCountFrequency (%)
0287
 
0.7%
114325
33.3%
29441
22.0%
36103
14.2%
43096
 
7.2%
53330
 
7.8%
ValueCountFrequency (%)
53330
 
7.8%
43096
 
7.2%
36103
14.2%
29441
22.0%
114325
33.3%
0287
 
0.7%

SEMIO_DOM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.732973325
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.019315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.556256132
Coefficient of variation (CV)0.3288115155
Kurtosis-0.5056451254
Mean4.732973325
Median Absolute Deviation (MAD)1
Skewness-0.5539731922
Sum203338
Variance2.421933148
MonotonicityNot monotonic
2021-12-29T16:00:21.111312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
612101
28.2%
511137
25.9%
38215
19.1%
74215
 
9.8%
43968
 
9.2%
21699
 
4.0%
11627
 
3.8%
ValueCountFrequency (%)
11627
 
3.8%
21699
 
4.0%
38215
19.1%
43968
 
9.2%
511137
25.9%
612101
28.2%
74215
 
9.8%
ValueCountFrequency (%)
74215
 
9.8%
612101
28.2%
511137
25.9%
43968
 
9.2%
38215
19.1%
21699
 
4.0%
11627
 
3.8%

SEMIO_ERL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.115893115
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.201265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.824980789
Coefficient of variation (CV)0.3567277009
Kurtosis-1.632432904
Mean5.115893115
Median Absolute Deviation (MAD)1
Skewness-0.2178947079
Sum219789
Variance3.33055488
MonotonicityNot monotonic
2021-12-29T16:00:21.274563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
717215
40.1%
312901
30.0%
65481
 
12.8%
45437
 
12.7%
2831
 
1.9%
5797
 
1.9%
1300
 
0.7%
ValueCountFrequency (%)
1300
 
0.7%
2831
 
1.9%
312901
30.0%
45437
 
12.7%
5797
 
1.9%
65481
 
12.8%
717215
40.1%
ValueCountFrequency (%)
717215
40.1%
65481
 
12.8%
5797
 
1.9%
45437
 
12.7%
312901
30.0%
2831
 
1.9%
1300
 
0.7%

SEMIO_FAM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.762557609
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.374727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.844612213
Coefficient of variation (CV)0.4902548758
Kurtosis-1.355286675
Mean3.762557609
Median Absolute Deviation (MAD)2
Skewness-0.010053423
Sum161647
Variance3.402594217
MonotonicityNot monotonic
2021-12-29T16:00:21.448553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
611209
26.1%
37526
17.5%
27303
17.0%
15982
13.9%
45393
12.6%
54594
10.7%
7955
 
2.2%
ValueCountFrequency (%)
15982
13.9%
27303
17.0%
37526
17.5%
45393
12.6%
54594
10.7%
611209
26.1%
7955
 
2.2%
ValueCountFrequency (%)
7955
 
2.2%
611209
26.1%
54594
10.7%
45393
12.6%
37526
17.5%
27303
17.0%
15982
13.9%

SEMIO_KAEM
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.665495089
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.540553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.723420523
Coefficient of variation (CV)0.3693971358
Kurtosis-0.8925927754
Mean4.665495089
Median Absolute Deviation (MAD)1
Skewness-0.5832553872
Sum200439
Variance2.970178299
MonotonicityNot monotonic
2021-12-29T16:00:21.632041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
615817
36.8%
38594
20.0%
57804
18.2%
73719
 
8.7%
23369
 
7.8%
12224
 
5.2%
41435
 
3.3%
ValueCountFrequency (%)
12224
 
5.2%
23369
 
7.8%
38594
20.0%
41435
 
3.3%
57804
18.2%
615817
36.8%
73719
 
8.7%
ValueCountFrequency (%)
73719
 
8.7%
615817
36.8%
57804
18.2%
41435
 
3.3%
38594
20.0%
23369
 
7.8%
12224
 
5.2%

SEMIO_KRIT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.005982031
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.733999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.866680663
Coefficient of variation (CV)0.3728900047
Kurtosis-0.9609347416
Mean5.005982031
Median Absolute Deviation (MAD)1
Skewness-0.4923246436
Sum215067
Variance3.484496697
MonotonicityNot monotonic
2021-12-29T16:00:21.805407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
713702
31.9%
39499
22.1%
68107
18.9%
45028
 
11.7%
53723
 
8.7%
12519
 
5.9%
2384
 
0.9%
ValueCountFrequency (%)
12519
 
5.9%
2384
 
0.9%
39499
22.1%
45028
 
11.7%
53723
 
8.7%
68107
18.9%
713702
31.9%
ValueCountFrequency (%)
713702
31.9%
68107
18.9%
53723
 
8.7%
45028
 
11.7%
39499
22.1%
2384
 
0.9%
12519
 
5.9%

SEMIO_KULT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.166658908
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:21.906879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.664364929
Coefficient of variation (CV)0.5255902127
Kurtosis-0.7438281921
Mean3.166658908
Median Absolute Deviation (MAD)1
Skewness0.3667172945
Sum136046
Variance2.770110617
MonotonicityNot monotonic
2021-12-29T16:00:21.977854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
311148
25.9%
19474
22.1%
47444
17.3%
25755
13.4%
64281
 
10.0%
53932
 
9.2%
7928
 
2.2%
ValueCountFrequency (%)
19474
22.1%
25755
13.4%
311148
25.9%
47444
17.3%
53932
 
9.2%
64281
 
10.0%
7928
 
2.2%
ValueCountFrequency (%)
7928
 
2.2%
64281
 
10.0%
53932
 
9.2%
47444
17.3%
311148
25.9%
25755
13.4%
19474
22.1%

SEMIO_LUST
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.399213258
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.069580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median5
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.523510448
Coefficient of variation (CV)0.282172675
Kurtosis0.7406617284
Mean5.399213258
Median Absolute Deviation (MAD)1
Skewness-0.9292804903
Sum231961
Variance2.321084086
MonotonicityNot monotonic
2021-12-29T16:00:22.140890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
514913
34.7%
714597
34.0%
45243
 
12.2%
64601
 
10.7%
11487
 
3.5%
21211
 
2.8%
3910
 
2.1%
ValueCountFrequency (%)
11487
 
3.5%
21211
 
2.8%
3910
 
2.1%
45243
 
12.2%
514913
34.7%
64601
 
10.7%
714597
34.0%
ValueCountFrequency (%)
714597
34.0%
64601
 
10.7%
514913
34.7%
45243
 
12.2%
3910
 
2.1%
21211
 
2.8%
11487
 
3.5%

SEMIO_MAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.417182627
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.232377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.806957964
Coefficient of variation (CV)0.5287858922
Kurtosis-1.276356166
Mean3.417182627
Median Absolute Deviation (MAD)2
Skewness0.07761872488
Sum146809
Variance3.265097083
MonotonicityNot monotonic
2021-12-29T16:00:22.313417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
59592
22.3%
19145
21.3%
27273
16.9%
45829
13.6%
35322
12.4%
64731
11.0%
71070
 
2.5%
ValueCountFrequency (%)
19145
21.3%
27273
16.9%
35322
12.4%
45829
13.6%
59592
22.3%
64731
11.0%
71070
 
2.5%
ValueCountFrequency (%)
71070
 
2.5%
64731
11.0%
59592
22.3%
45829
13.6%
35322
12.4%
27273
16.9%
19145
21.3%

SEMIO_PFLICHT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.345724128
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.405393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.570618056
Coefficient of variation (CV)0.4694403948
Kurtosis-0.8087919765
Mean3.345724128
Median Absolute Deviation (MAD)1
Skewness0.1547572243
Sum143739
Variance2.466841077
MonotonicityNot monotonic
2021-12-29T16:00:22.486977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
59671
22.5%
29155
21.3%
48912
20.7%
36766
15.7%
16186
14.4%
71310
 
3.0%
6962
 
2.2%
ValueCountFrequency (%)
16186
14.4%
29155
21.3%
36766
15.7%
48912
20.7%
59671
22.5%
6962
 
2.2%
71310
 
3.0%
ValueCountFrequency (%)
71310
 
3.0%
6962
 
2.2%
59671
22.5%
48912
20.7%
36766
15.7%
29155
21.3%
16186
14.4%

SEMIO_RAT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.196266468
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.568772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.278979216
Coefficient of variation (CV)0.4001478689
Kurtosis0.6106085905
Mean3.196266468
Median Absolute Deviation (MAD)1
Skewness0.4537494497
Sum137318
Variance1.635787834
MonotonicityNot monotonic
2021-12-29T16:00:22.650287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
413563
31.6%
312671
29.5%
28539
19.9%
13991
 
9.3%
52255
 
5.2%
71051
 
2.4%
6892
 
2.1%
ValueCountFrequency (%)
13991
 
9.3%
28539
19.9%
312671
29.5%
413563
31.6%
52255
 
5.2%
6892
 
2.1%
71051
 
2.4%
ValueCountFrequency (%)
71051
 
2.4%
6892
 
2.1%
52255
 
5.2%
413563
31.6%
312671
29.5%
28539
19.9%
13991
 
9.3%

SEMIO_REL
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.471789023
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.740369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.096218519
Coefficient of variation (CV)0.6037862628
Kurtosis-0.9603598458
Mean3.471789023
Median Absolute Deviation (MAD)1
Skewness0.5465523515
Sum149155
Variance4.39413208
MonotonicityNot monotonic
2021-12-29T16:00:22.804117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
19217
21.5%
38366
19.5%
78227
19.1%
27626
17.8%
46132
14.3%
52893
 
6.7%
6501
 
1.2%
ValueCountFrequency (%)
19217
21.5%
27626
17.8%
38366
19.5%
46132
14.3%
52893
 
6.7%
6501
 
1.2%
78227
19.1%
ValueCountFrequency (%)
78227
19.1%
6501
 
1.2%
52893
 
6.7%
46132
14.3%
38366
19.5%
27626
17.8%
19217
21.5%

SEMIO_SOZ
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.624854523
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:22.886199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.67794576
Coefficient of variation (CV)0.462900166
Kurtosis-1.222652853
Mean3.624854523
Median Absolute Deviation (MAD)1
Skewness0.2320793429
Sum155731
Variance2.815501975
MonotonicityNot monotonic
2021-12-29T16:00:22.957424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
212861
29.9%
67619
17.7%
46823
15.9%
36074
14.1%
55907
13.7%
12750
 
6.4%
7928
 
2.2%
ValueCountFrequency (%)
12750
 
6.4%
212861
29.9%
36074
14.1%
46823
15.9%
55907
13.7%
67619
17.7%
7928
 
2.2%
ValueCountFrequency (%)
7928
 
2.2%
67619
17.7%
55907
13.7%
46823
15.9%
36074
14.1%
212861
29.9%
12750
 
6.4%

SEMIO_TRADV
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.785554676
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:23.047223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.338822988
Coefficient of variation (CV)0.4806306618
Kurtosis0.609000932
Mean2.785554676
Median Absolute Deviation (MAD)1
Skewness0.5536524565
Sum119673
Variance1.792446992
MonotonicityNot monotonic
2021-12-29T16:00:23.141729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
316411
38.2%
110009
23.3%
48373
19.5%
25486
 
12.8%
6958
 
2.2%
5928
 
2.2%
7797
 
1.9%
ValueCountFrequency (%)
110009
23.3%
25486
 
12.8%
316411
38.2%
48373
19.5%
5928
 
2.2%
6958
 
2.2%
7797
 
1.9%
ValueCountFrequency (%)
7797
 
1.9%
6958
 
2.2%
5928
 
2.2%
48373
19.5%
316411
38.2%
25486
 
12.8%
110009
23.3%

SEMIO_VERT
Real number (ℝ≥0)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.886620735
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:23.233505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.103138486
Coefficient of variation (CV)0.5411226434
Kurtosis-1.337630141
Mean3.886620735
Median Absolute Deviation (MAD)2
Skewness0.01014991775
Sum166977
Variance4.42319149
MonotonicityNot monotonic
2021-12-29T16:00:23.305016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
18661
20.2%
46724
15.7%
76392
14.9%
56127
14.3%
25762
13.4%
65543
12.9%
33753
8.7%
ValueCountFrequency (%)
18661
20.2%
25762
13.4%
33753
8.7%
46724
15.7%
56127
14.3%
65543
12.9%
76392
14.9%
ValueCountFrequency (%)
76392
14.9%
65543
12.9%
56127
14.3%
46724
15.7%
33753
8.7%
25762
13.4%
18661
20.2%

SHOPPER_TYP
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
3
13807 
1
9305 
-1
7397 
2
6419 
0
6034 

Length

Max length2
Median length1
Mean length1.172175411
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
313807
32.1%
19305
21.7%
-17397
17.2%
26419
14.9%
06034
14.0%

Length

2021-12-29T16:00:23.397349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:23.469033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
116702
38.9%
313807
32.1%
26419
 
14.9%
06034
 
14.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SOHO_KZ
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Memory size335.8 KiB
0.0
35645 
1.0
 
348

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035645
83.0%
1.0348
 
0.8%
(Missing)6969
 
16.2%

Length

2021-12-29T16:00:23.555195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:23.619108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035645
99.0%
1.0348
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STRUKTURTYP
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing7955
Missing (%)18.5%
Memory size335.8 KiB
3.0
23303 
1.0
6569 
2.0
5135 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.023303
54.2%
1.06569
 
15.3%
2.05135
 
12.0%
(Missing)7955
 
18.5%

Length

2021-12-29T16:00:23.682510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:23.754299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.023303
66.6%
1.06569
 
18.8%
2.05135
 
14.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TITEL_KZ
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Memory size335.8 KiB
0.0
35780 
1.0
 
190
4.0
 
10
5.0
 
8
3.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.035780
83.3%
1.0190
 
0.4%
4.010
 
< 0.1%
5.08
 
< 0.1%
3.05
 
< 0.1%
(Missing)6969
 
16.2%

Length

2021-12-29T16:00:23.825334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:23.916154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.035780
99.4%
1.0190
 
0.5%
4.010
 
< 0.1%
5.08
 
< 0.1%
3.05
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UMFELD_ALT
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7925
Missing (%)18.4%
Memory size335.8 KiB
3.0
9342 
4.0
7576 
1.0
6985 
2.0
6870 
5.0
4264 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.09342
21.7%
4.07576
17.6%
1.06985
16.3%
2.06870
16.0%
5.04264
9.9%
(Missing)7925
18.4%

Length

2021-12-29T16:00:24.007209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:24.108737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
3.09342
26.7%
4.07576
21.6%
1.06985
19.9%
2.06870
19.6%
5.04264
12.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UMFELD_JUNG
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing7925
Missing (%)18.4%
Memory size335.8 KiB
5.0
19190 
4.0
9282 
3.0
4188 
2.0
 
1539
1.0
 
838

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.019190
44.7%
4.09282
21.6%
3.04188
 
9.7%
2.01539
 
3.6%
1.0838
 
2.0%
(Missing)7925
18.4%

Length

2021-12-29T16:00:24.208798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:24.282806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.019190
54.8%
4.09282
26.5%
3.04188
 
12.0%
2.01539
 
4.4%
1.0838
 
2.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UNGLEICHENN_FLAG
Categorical

MISSING

Distinct2
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Memory size335.8 KiB
0.0
33428 
1.0
 
2565

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.033428
77.8%
1.02565
 
6.0%
(Missing)6969
 
16.2%

Length

2021-12-29T16:00:24.374225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:24.455116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.033428
92.9%
1.02565
 
7.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VERDICHTUNGSRAUM
Real number (ℝ≥0)

MISSING
ZEROS

Distinct46
Distinct (%)0.1%
Missing7955
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean4.465992516
Minimum0
Maximum45
Zeros17087
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:24.534003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile25
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.460627756
Coefficient of variation (CV)1.894456322
Kurtosis6.943314325
Mean4.465992516
Median Absolute Deviation (MAD)1
Skewness2.634332856
Sum156341
Variance71.58222202
MonotonicityNot monotonic
2021-12-29T16:00:24.647652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
017087
39.8%
13804
 
8.9%
22403
 
5.6%
31656
 
3.9%
41364
 
3.2%
6892
 
2.1%
5870
 
2.0%
9507
 
1.2%
8504
 
1.2%
7493
 
1.1%
Other values (36)5427
 
12.6%
(Missing)7955
18.5%
ValueCountFrequency (%)
017087
39.8%
13804
 
8.9%
22403
 
5.6%
31656
 
3.9%
41364
 
3.2%
5870
 
2.0%
6892
 
2.1%
7493
 
1.1%
8504
 
1.2%
9507
 
1.2%
ValueCountFrequency (%)
4570
0.2%
4481
0.2%
4395
0.2%
4246
0.1%
4126
 
0.1%
4052
0.1%
3955
0.1%
3882
0.2%
3771
0.2%
36104
0.2%

VERS_TYP
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
1
18540 
2
17025 
-1
7397 

Length

Max length2
Median length1
Mean length1.172175411
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
118540
43.2%
217025
39.6%
-17397
 
17.2%

Length

2021-12-29T16:00:24.767159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:24.830685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
125937
60.4%
217025
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VHA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean1.137443392
Minimum0
Maximum5
Zeros16176
Zeros (%)37.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:24.902240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.553994733
Coefficient of variation (CV)1.366217207
Kurtosis0.9954126717
Mean1.137443392
Median Absolute Deviation (MAD)1
Skewness1.502467609
Sum40940
Variance2.414899632
MonotonicityNot monotonic
2021-12-29T16:00:25.004176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
016176
37.7%
113027
30.3%
53020
 
7.0%
31761
 
4.1%
41756
 
4.1%
2253
 
0.6%
(Missing)6969
16.2%
ValueCountFrequency (%)
016176
37.7%
113027
30.3%
2253
 
0.6%
31761
 
4.1%
41756
 
4.1%
53020
 
7.0%
ValueCountFrequency (%)
53020
 
7.0%
41756
 
4.1%
31761
 
4.1%
2253
 
0.6%
113027
30.3%
016176
37.7%

VHN
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing8445
Missing (%)19.7%
Memory size335.8 KiB
2.0
10989 
3.0
7829 
4.0
7545 
1.0
6669 
0.0
1485 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.010989
25.6%
3.07829
18.2%
4.07545
17.6%
1.06669
15.5%
0.01485
 
3.5%
(Missing)8445
19.7%

Length

2021-12-29T16:00:25.116390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:25.187404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.010989
31.8%
3.07829
22.7%
4.07545
21.9%
1.06669
19.3%
0.01485
 
4.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

VK_DHT4A
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing7267
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean4.318644068
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:25.268510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.165198775
Coefficient of variation (CV)0.732914944
Kurtosis-1.243628625
Mean4.318644068
Median Absolute Deviation (MAD)2
Skewness0.4549453147
Sum154154
Variance10.01848328
MonotonicityNot monotonic
2021-12-29T16:00:25.349656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
110900
25.4%
24387
10.2%
73017
 
7.0%
102941
 
6.8%
62795
 
6.5%
32619
 
6.1%
92441
 
5.7%
42271
 
5.3%
82201
 
5.1%
52115
 
4.9%
(Missing)7267
16.9%
ValueCountFrequency (%)
110900
25.4%
24387
10.2%
32619
 
6.1%
42271
 
5.3%
52115
 
4.9%
62795
 
6.5%
73017
 
7.0%
82201
 
5.1%
92441
 
5.7%
102941
 
6.8%
ValueCountFrequency (%)
118
 
< 0.1%
102941
6.8%
92441
5.7%
82201
5.1%
73017
7.0%
62795
6.5%
52115
4.9%
42271
5.3%
32619
6.1%
24387
10.2%

VK_DISTANZ
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)< 0.1%
Missing7267
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean4.505953215
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:25.430614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile11
Maximum13
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.289502061
Coefficient of variation (CV)0.7300346683
Kurtosis-0.5301004379
Mean4.505953215
Median Absolute Deviation (MAD)3
Skewness0.7047166047
Sum160840
Variance10.82082381
MonotonicityNot monotonic
2021-12-29T16:00:25.522533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
18652
20.1%
25362
12.5%
64160
9.7%
33482
8.1%
43005
 
7.0%
72969
 
6.9%
81966
 
4.6%
91517
 
3.5%
51198
 
2.8%
101049
 
2.4%
Other values (3)2335
 
5.4%
(Missing)7267
16.9%
ValueCountFrequency (%)
18652
20.1%
25362
12.5%
33482
8.1%
43005
 
7.0%
51198
 
2.8%
64160
9.7%
72969
 
6.9%
81966
 
4.6%
91517
 
3.5%
101049
 
2.4%
ValueCountFrequency (%)
13412
 
1.0%
12885
 
2.1%
111038
 
2.4%
101049
 
2.4%
91517
 
3.5%
81966
4.6%
72969
6.9%
64160
9.7%
51198
 
2.8%
43005
7.0%

VK_ZG11
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)< 0.1%
Missing7267
Missing (%)16.9%
Infinite0
Infinite (%)0.0%
Mean3.11696316
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:25.614321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile9
Maximum11
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.534331308
Coefficient of variation (CV)0.813077081
Kurtosis1.015065081
Mean3.11696316
Median Absolute Deviation (MAD)1
Skewness1.33750966
Sum111260
Variance6.422835177
MonotonicityNot monotonic
2021-12-29T16:00:25.696430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
113344
31.1%
25703
13.3%
35180
 
12.1%
43554
 
8.3%
52190
 
5.1%
61511
 
3.5%
9970
 
2.3%
7960
 
2.2%
10957
 
2.2%
8956
 
2.2%
(Missing)7267
16.9%
ValueCountFrequency (%)
113344
31.1%
25703
13.3%
35180
 
12.1%
43554
 
8.3%
52190
 
5.1%
61511
 
3.5%
7960
 
2.2%
8956
 
2.2%
9970
 
2.3%
10957
 
2.2%
ValueCountFrequency (%)
11370
 
0.9%
10957
 
2.2%
9970
 
2.3%
8956
 
2.2%
7960
 
2.2%
61511
 
3.5%
52190
 
5.1%
43554
8.3%
35180
12.1%
25703
13.3%

W_KEIT_KIND_HH
Real number (ℝ≥0)

MISSING
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing9678
Missing (%)22.5%
Infinite0
Infinite (%)0.0%
Mean4.488402836
Minimum0
Maximum6
Zeros739
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:25.788828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.889573269
Coefficient of variation (CV)0.4209901246
Kurtosis-0.8619692857
Mean4.488402836
Median Absolute Deviation (MAD)0
Skewness-0.7885563894
Sum149392
Variance3.57048714
MonotonicityNot monotonic
2021-12-29T16:00:25.860405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
618152
42.3%
24739
 
11.0%
43418
 
8.0%
32637
 
6.1%
12144
 
5.0%
51455
 
3.4%
0739
 
1.7%
(Missing)9678
22.5%
ValueCountFrequency (%)
0739
 
1.7%
12144
 
5.0%
24739
 
11.0%
32637
 
6.1%
43418
 
8.0%
51455
 
3.4%
618152
42.3%
ValueCountFrequency (%)
618152
42.3%
51455
 
3.4%
43418
 
8.0%
32637
 
6.1%
24739
 
11.0%
12144
 
5.0%
0739
 
1.7%

WOHNDAUER_2008
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing6969
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean8.72994749
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:25.952625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q19
median9
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.010545222
Coefficient of variation (CV)0.1157561627
Kurtosis19.35655156
Mean8.72994749
Median Absolute Deviation (MAD)0
Skewness-4.360986433
Sum314217
Variance1.021201645
MonotonicityNot monotonic
2021-12-29T16:00:26.034366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
932478
75.6%
81509
 
3.5%
4461
 
1.1%
6419
 
1.0%
5382
 
0.9%
7354
 
0.8%
3346
 
0.8%
129
 
0.1%
215
 
< 0.1%
(Missing)6969
 
16.2%
ValueCountFrequency (%)
129
 
0.1%
215
 
< 0.1%
3346
 
0.8%
4461
 
1.1%
5382
 
0.9%
6419
 
1.0%
7354
 
0.8%
81509
 
3.5%
932478
75.6%
ValueCountFrequency (%)
932478
75.6%
81509
 
3.5%
7354
 
0.8%
6419
 
1.0%
5382
 
0.9%
4461
 
1.1%
3346
 
0.8%
215
 
< 0.1%
129
 
0.1%

WOHNLAGE
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing7777
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean4.059684525
Minimum0
Maximum8
Zeros127
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:26.126472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q37
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.046696937
Coefficient of variation (CV)0.5041517203
Kurtosis-1.129424703
Mean4.059684525
Median Absolute Deviation (MAD)1
Skewness0.4193841576
Sum142840
Variance4.188968352
MonotonicityNot monotonic
2021-12-29T16:00:26.208150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
311342
26.4%
79124
21.2%
25001
11.6%
44559
10.6%
12473
 
5.8%
52079
 
4.8%
8480
 
1.1%
0127
 
0.3%
(Missing)7777
18.1%
ValueCountFrequency (%)
0127
 
0.3%
12473
 
5.8%
25001
11.6%
311342
26.4%
44559
10.6%
52079
 
4.8%
79124
21.2%
8480
 
1.1%
ValueCountFrequency (%)
8480
 
1.1%
79124
21.2%
52079
 
4.8%
44559
10.6%
311342
26.4%
25001
11.6%
12473
 
5.8%
0127
 
0.3%

ZABEOTYP
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.80419906
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.8 KiB
2021-12-29T16:00:26.297357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.121585077
Coefficient of variation (CV)0.3999662839
Kurtosis1.15885187
Mean2.80419906
Median Absolute Deviation (MAD)0
Skewness0.2647619627
Sum120474
Variance1.257953085
MonotonicityNot monotonic
2021-12-29T16:00:26.378122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
326958
62.7%
18385
 
19.5%
44133
 
9.6%
61705
 
4.0%
21484
 
3.5%
5297
 
0.7%
ValueCountFrequency (%)
18385
 
19.5%
21484
 
3.5%
326958
62.7%
44133
 
9.6%
5297
 
0.7%
61705
 
4.0%
ValueCountFrequency (%)
61705
 
4.0%
5297
 
0.7%
44133
 
9.6%
326958
62.7%
21484
 
3.5%
18385
 
19.5%

RESPONSE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
0
42430 
1
 
532

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
042430
98.8%
1532
 
1.2%

Length

2021-12-29T16:00:26.468729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:26.521663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
042430
98.8%
1532
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ANREDE_KZ
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
2
25566 
1
17396 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
225566
59.5%
117396
40.5%

Length

2021-12-29T16:00:26.590549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:26.664180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
225566
59.5%
117396
40.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ALTERSKATEGORIE_GROB
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size335.8 KiB
4
22906 
3
11221 
1
5448 
2
3306 
9
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row4
4th row4
5th row3

Common Values

ValueCountFrequency (%)
422906
53.3%
311221
26.1%
15448
 
12.7%
23306
 
7.7%
981
 
0.2%

Length

2021-12-29T16:00:26.735792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-29T16:00:26.796923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
422906
53.3%
311221
26.1%
15448
 
12.7%
23306
 
7.7%
981
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Sample

First rows

LNRAGER_TYPAKT_DAT_KLALTER_HHALTER_KIND1ALTER_KIND2ALTER_KIND3ALTER_KIND4ALTERSKATEGORIE_FEINANZ_HAUSHALTE_AKTIVANZ_HH_TITELANZ_KINDERANZ_PERSONENANZ_STATISTISCHE_HAUSHALTEANZ_TITELARBEITBALLRAUMCAMEO_DEU_2015CAMEO_DEUG_2015CAMEO_INTL_2015CJT_GESAMTTYPCJT_KATALOGNUTZERCJT_TYP_1CJT_TYP_2CJT_TYP_3CJT_TYP_4CJT_TYP_5CJT_TYP_6D19_BANKEN_ANZ_12D19_BANKEN_ANZ_24D19_BANKEN_DATUMD19_BANKEN_DIREKTD19_BANKEN_GROSSD19_BANKEN_LOKALD19_BANKEN_OFFLINE_DATUMD19_BANKEN_ONLINE_DATUMD19_BANKEN_ONLINE_QUOTE_12D19_BANKEN_RESTD19_BEKLEIDUNG_GEHD19_BEKLEIDUNG_RESTD19_BILDUNGD19_BIO_OEKOD19_BUCH_CDD19_DIGIT_SERVD19_DROGERIEARTIKELD19_ENERGIED19_FREIZEITD19_GARTEND19_GESAMT_ANZ_12D19_GESAMT_ANZ_24D19_GESAMT_DATUMD19_GESAMT_OFFLINE_DATUMD19_GESAMT_ONLINE_DATUMD19_GESAMT_ONLINE_QUOTE_12D19_HANDWERKD19_HAUS_DEKOD19_KINDERARTIKELD19_KONSUMTYPD19_KONSUMTYP_MAXD19_KOSMETIKD19_LEBENSMITTELD19_LETZTER_KAUF_BRANCHED19_LOTTOD19_NAHRUNGSERGAENZUNGD19_RATGEBERD19_REISEND19_SAMMELARTIKELD19_SCHUHED19_SONSTIGED19_SOZIALESD19_TECHNIKD19_TELKO_ANZ_12D19_TELKO_ANZ_24D19_TELKO_DATUMD19_TELKO_MOBILED19_TELKO_OFFLINE_DATUMD19_TELKO_ONLINE_DATUMD19_TELKO_ONLINE_QUOTE_12D19_TELKO_RESTD19_TIERARTIKELD19_VERSAND_ANZ_12D19_VERSAND_ANZ_24D19_VERSAND_DATUMD19_VERSAND_OFFLINE_DATUMD19_VERSAND_ONLINE_DATUMD19_VERSAND_ONLINE_QUOTE_12D19_VERSAND_RESTD19_VERSI_ANZ_12D19_VERSI_ANZ_24D19_VERSI_DATUMD19_VERSI_OFFLINE_DATUMD19_VERSI_ONLINE_DATUMD19_VERSI_ONLINE_QUOTE_12D19_VERSICHERUNGEND19_VOLLSORTIMENTD19_WEIN_FEINKOSTDSL_FLAGEINGEFUEGT_AMEINGEZOGENAM_HH_JAHREWDICHTEEXTSEL992FINANZ_ANLEGERFINANZ_HAUSBAUERFINANZ_MINIMALISTFINANZ_SPARERFINANZ_UNAUFFAELLIGERFINANZ_VORSORGERFINANZTYPFIRMENDICHTEGEBAEUDETYPGEBAEUDETYP_RASTERGEBURTSJAHRGEMEINDETYPGFK_URLAUBERTYPGREEN_AVANTGARDEHEALTH_TYPHH_DELTA_FLAGHH_EINKOMMEN_SCOREINNENSTADTKBA05_ALTER1KBA05_ALTER2KBA05_ALTER3KBA05_ALTER4KBA05_ANHANGKBA05_ANTG1KBA05_ANTG2KBA05_ANTG3KBA05_ANTG4KBA05_AUTOQUOTKBA05_BAUMAXKBA05_CCM1KBA05_CCM2KBA05_CCM3KBA05_CCM4KBA05_DIESELKBA05_FRAUKBA05_GBZKBA05_HERST1KBA05_HERST2KBA05_HERST3KBA05_HERST4KBA05_HERST5KBA05_HERSTTEMPKBA05_KRSAQUOTKBA05_KRSHERST1KBA05_KRSHERST2KBA05_KRSHERST3KBA05_KRSKLEINKBA05_KRSOBERKBA05_KRSVANKBA05_KRSZULKBA05_KW1KBA05_KW2KBA05_KW3KBA05_MAXAHKBA05_MAXBJKBA05_MAXHERSTKBA05_MAXSEGKBA05_MAXVORBKBA05_MOD1KBA05_MOD2KBA05_MOD3KBA05_MOD4KBA05_MOD8KBA05_MODTEMPKBA05_MOTORKBA05_MOTRADKBA05_SEG1KBA05_SEG10KBA05_SEG2KBA05_SEG3KBA05_SEG4KBA05_SEG5KBA05_SEG6KBA05_SEG7KBA05_SEG8KBA05_SEG9KBA05_VORB0KBA05_VORB1KBA05_VORB2KBA05_ZUL1KBA05_ZUL2KBA05_ZUL3KBA05_ZUL4KBA13_ALTERHALTER_30KBA13_ALTERHALTER_45KBA13_ALTERHALTER_60KBA13_ALTERHALTER_61KBA13_ANTG1KBA13_ANTG2KBA13_ANTG3KBA13_ANTG4KBA13_ANZAHL_PKWKBA13_AUDIKBA13_AUTOQUOTEKBA13_BAUMAXKBA13_BJ_1999KBA13_BJ_2000KBA13_BJ_2004KBA13_BJ_2006KBA13_BJ_2008KBA13_BJ_2009KBA13_BMWKBA13_CCM_0_1400KBA13_CCM_1000KBA13_CCM_1200KBA13_CCM_1400KBA13_CCM_1401_2500KBA13_CCM_1500KBA13_CCM_1600KBA13_CCM_1800KBA13_CCM_2000KBA13_CCM_2500KBA13_CCM_2501KBA13_CCM_3000KBA13_CCM_3001KBA13_FAB_ASIENKBA13_FAB_SONSTIGEKBA13_FIATKBA13_FORDKBA13_GBZKBA13_HALTER_20KBA13_HALTER_25KBA13_HALTER_30KBA13_HALTER_35KBA13_HALTER_40KBA13_HALTER_45KBA13_HALTER_50KBA13_HALTER_55KBA13_HALTER_60KBA13_HALTER_65KBA13_HALTER_66KBA13_HERST_ASIENKBA13_HERST_AUDI_VWKBA13_HERST_BMW_BENZKBA13_HERST_EUROPAKBA13_HERST_FORD_OPELKBA13_HERST_SONSTKBA13_HHZKBA13_KMH_0_140KBA13_KMH_110KBA13_KMH_140KBA13_KMH_140_210KBA13_KMH_180KBA13_KMH_210KBA13_KMH_211KBA13_KMH_250KBA13_KMH_251KBA13_KRSAQUOTKBA13_KRSHERST_AUDI_VWKBA13_KRSHERST_BMW_BENZKBA13_KRSHERST_FORD_OPELKBA13_KRSSEG_KLEINKBA13_KRSSEG_OBERKBA13_KRSSEG_VANKBA13_KRSZUL_NEUKBA13_KW_0_60KBA13_KW_110KBA13_KW_120KBA13_KW_121KBA13_KW_30KBA13_KW_40KBA13_KW_50KBA13_KW_60KBA13_KW_61_120KBA13_KW_70KBA13_KW_80KBA13_KW_90KBA13_MAZDAKBA13_MERCEDESKBA13_MOTORKBA13_NISSANKBA13_OPELKBA13_PEUGEOTKBA13_RENAULTKBA13_SEG_GELAENDEWAGENKBA13_SEG_GROSSRAUMVANSKBA13_SEG_KLEINSTKBA13_SEG_KLEINWAGENKBA13_SEG_KOMPAKTKLASSEKBA13_SEG_MINIVANSKBA13_SEG_MINIWAGENKBA13_SEG_MITTELKLASSEKBA13_SEG_OBEREMITTELKLASSEKBA13_SEG_OBERKLASSEKBA13_SEG_SONSTIGEKBA13_SEG_SPORTWAGENKBA13_SEG_UTILITIESKBA13_SEG_VANKBA13_SEG_WOHNMOBILEKBA13_SITZE_4KBA13_SITZE_5KBA13_SITZE_6KBA13_TOYOTAKBA13_VORB_0KBA13_VORB_1KBA13_VORB_1_2KBA13_VORB_2KBA13_VORB_3KBA13_VWKK_KUNDENTYPKKKKOMBIALTERKONSUMNAEHEKONSUMZELLELP_FAMILIE_FEINLP_FAMILIE_GROBLP_LEBENSPHASE_FEINLP_LEBENSPHASE_GROBLP_STATUS_FEINLP_STATUS_GROBMIN_GEBAEUDEJAHRMOBI_RASTERMOBI_REGIONATIONALITAET_KZONLINE_AFFINITAETORTSGR_KLS9OST_WEST_KZPLZ8_ANTG1PLZ8_ANTG2PLZ8_ANTG3PLZ8_ANTG4PLZ8_BAUMAXPLZ8_GBZPLZ8_HHZPRAEGENDE_JUGENDJAHREREGIOTYPRELAT_ABRETOURTYP_BK_SRT_KEIN_ANREIZRT_SCHNAEPPCHENRT_UEBERGROESSESEMIO_DOMSEMIO_ERLSEMIO_FAMSEMIO_KAEMSEMIO_KRITSEMIO_KULTSEMIO_LUSTSEMIO_MATSEMIO_PFLICHTSEMIO_RATSEMIO_RELSEMIO_SOZSEMIO_TRADVSEMIO_VERTSHOPPER_TYPSOHO_KZSTRUKTURTYPTITEL_KZUMFELD_ALTUMFELD_JUNGUNGLEICHENN_FLAGVERDICHTUNGSRAUMVERS_TYPVHAVHNVK_DHT4AVK_DISTANZVK_ZG11W_KEIT_KIND_HHWOHNDAUER_2008WOHNLAGEZABEOTYPRESPONSEANREDE_KZALTERSKATEGORIE_GROB
0176321.08.0NaNNaNNaNNaN8.015.00.00.01.013.00.03.05.05D5342.05.02.02.05.05.05.05.0001000010100.000000000000009990.00003.0270D19_UNBEKANNT0.00000001.000010010100.000009990.0000910100.06601.01992-02-10 00:00:002004.05.047.025311562.03.03.0012.08.0020.06.02.01.03.03.04.00.00.00.00.02.03.04.01.05.02.03.03.03.01.04.02.04.01.03.04.03.04.02.05.01.02.02.01.01.04.02.05.03.03.02.03.03.03.05.01.00.04.02.01.00.02.02.03.03.02.00.00.02.00.03.03.03.03.04.03.01.04.04.01.04.01.04.03.01.0390.05.02.05.03.03.04.03.02.03.04.02.03.02.03.03.01.04.03.03.03.04.03.05.02.02.05.03.02.03.04.04.05.04.02.02.01.01.04.04.02.03.04.05.02.02.03.03.01.04.02.03.02.03.03.01.02.03.03.03.02.02.01.02.02.04.01.03.03.03.02.02.04.04.03.02.02.03.03.03.02.04.03.03.01.02.02.03.01.02.05.04.00.03.05.04.01.00.03.03.03.03.03.04.04.04.02.03.0NaN3.041.01.01.01.08.02.03.02.01992.01.01.011.07.0W1.04.02.01.05.02.03.015.05.05.02.05.01.06726617132151330.03.00.02.04.00.04.021.02.05.02.01.06.09.03.03024
1177114.013.0NaNNaNNaNNaN13.01.00.00.02.01.00.02.05.05B5322.02.02.02.04.03.05.04.011500310100.000600006000221180.06601.0460D19_TELKO_MOBILE0.00000065.0011131100.006115580.00001010100.00631.01997-05-14 00:00:001994.01.056.023511564.01.04.0195740.08.0020.01.07.03.01.05.03.03.03.00.00.00.03.01.03.03.03.01.03.03.05.01.04.04.01.02.02.02.02.04.04.02.01.03.02.03.04.00.03.01.02.03.03.00.05.01.03.02.02.03.01.02.02.03.01.05.00.00.00.00.02.02.05.02.03.03.04.01.02.03.04.03.03.02.01.00.0586.03.04.01.04.03.03.03.02.03.01.00.03.00.03.04.01.02.04.04.03.02.02.01.03.02.02.04.04.03.02.02.01.03.04.04.04.03.03.03.03.05.02.02.03.02.03.04.03.04.03.03.03.02.02.01.04.04.02.03.02.03.02.02.03.03.03.02.03.03.02.00.04.03.04.03.02.03.04.03.03.03.03.03.04.03.03.02.03.04.04.03.01.03.02.05.03.00.01.04.05.04.03.03.04.03.03.05.02.02.044.00.02.02.019.05.09.04.01994.04.05.022.02.0W3.02.01.00.01.04.03.083.01.02.03.01.03.06615636554313420.01.00.02.05.00.00.011.03.01.02.01.04.09.07.01023
2177611.09.0NaNNaNNaNNaN7.00.0NaN0.00.01.00.04.01.02D2144.05.01.01.05.05.05.05.00170001070.050000000000026670.00002.0205D19_LEBENSMITTEL0.00000062.060010010100.0000166100.00001010100.00601.01995-05-24 00:00:001997.06.036.012511454.02.04.0192911.03.011NaN1.03.01.03.01.05.01.03.00.00.00.04.01.03.03.02.01.02.03.05.02.03.03.04.02.03.05.04.03.03.03.02.02.02.03.03.01.05.04.04.01.02.01.02.04.04.00.04.02.00.01.01.04.03.02.02.00.00.00.01.03.04.01.03.02.04.02.05.04.01.03.04.01.00.00.0297.04.02.01.03.02.03.03.05.02.03.02.02.00.04.04.03.03.00.05.02.02.02.01.02.04.01.03.04.03.05.05.05.04.03.02.01.01.02.03.03.03.03.04.03.04.03.01.01.01.04.02.05.02.02.01.03.04.04.02.02.02.02.02.03.04.03.02.01.02.03.03.04.01.03.04.04.03.02.03.03.05.04.02.02.03.03.04.02.02.05.02.00.05.02.02.02.02.02.04.02.03.03.03.03.03.02.02.0NaN1.042.00.00.00.00.00.010.05.01995.05.05.013.08.0O4.00.00.01.01.04.03.021.04.03.01.05.02.03341347611531730.03.00.01.05.00.010.014.01.06.04.02.0NaN9.02.03014
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